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Wang T, Zou Y, Meng H, Zheng P, Teng J, Huang N, Chen J. Securin acetylation prevents precocious separase activation and premature sister chromatid separation. Curr Biol 2024; 34:1295-1308.e5. [PMID: 38452759 DOI: 10.1016/j.cub.2024.02.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 01/08/2024] [Accepted: 02/15/2024] [Indexed: 03/09/2024]
Abstract
Lysine acetylation of non-histone proteins plays crucial roles in many cellular processes. In this study, we examine the role of lysine acetylation during sister chromatid separation in mitosis. We investigate the acetylation of securin at K21 by cell-cycle-dependent acetylome analysis and uncover its role in separase-triggered chromosome segregation during mitosis. Prior to the onset of anaphase, the acetylated securin via TIP60 prevents its degradation by the APC/CCDC20-mediated ubiquitin-proteasome system. This, in turn, restrains precocious activation of separase and premature separation of sister chromatids. Additionally, the acetylation-dependent stability of securin is also enhanced by its dephosphorylation. As anaphase approaches, HDAC1-mediated deacetylation of securin promotes its degradation, allowing released separase to cleave centromeric cohesin. Blocking securin deacetylation leads to longer anaphase duration and errors in chromosome segregation. Thus, this study illustrates the emerging role of securin acetylation dynamics in mitotic progression and genetic stability.
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Affiliation(s)
- Tianning Wang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, College of Life Sciences, Peking University, Beijing 100871, China; Breast Disease Diagnosis and Treatment Center/Department of Thyroid Surgery, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, China; Research Center of Translational Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, China
| | - Yuhong Zou
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, College of Life Sciences, Peking University, Beijing 100871, China
| | - Hui Meng
- Institute of Neuroscience, Translational Medicine Institute, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China; Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China
| | - Pengli Zheng
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, College of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Junlin Teng
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, College of Life Sciences, Peking University, Beijing 100871, China.
| | - Ning Huang
- Institute of Neuroscience, Translational Medicine Institute, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China; Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an 710061, China.
| | - Jianguo Chen
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, College of Life Sciences, Peking University, Beijing 100871, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
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Wang GA, Yan X, Li X, Liu Y, Xia J, Zhu X. MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy. ACS OMEGA 2023; 8:41930-41942. [PMID: 37969991 PMCID: PMC10634282 DOI: 10.1021/acsomega.3c07086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 11/17/2023]
Abstract
As one of the most important post-translational modifications (PTM), lysine acetylation (Kace) plays an important role in various biological activities. Traditional experimental methods for identifying Kace sites are inefficient and expensive. Instead, several machine learning methods have been developed for Kace site prediction, and hand-crafted features have been used to encode the protein sequences. However, there are still two challenges: the complex biological information may be under-represented by these manmade features and the small sample issue of some species needs to be addressed. We propose a novel model, MSTL-Kace, which was developed based on transfer learning strategy with pretrained bidirectional encoder representations from transformers (BERT) model. In this model, the high-level embeddings were extracted from species-specific BERT models, and a two-stage fine-tuning strategy was used to deal with small sample issue. Specifically, a domain-specific BERT model was pretrained using all of the sequences in our data sets, which was then fine-tuned, or two-stage fine-tuned based on the training data set of each species to obtain the species-specific BERT models. Afterward, the embeddings of residues were extracted from the fine-tuned model and fed to the different downstream learning algorithms. After comparison, the best model for the six prokaryotic species was built by using a random forest. The results for the independent test sets show that our model outperforms the state-of-the-art methods on all six species. The source codes and data for MSTL-Kace are available at https://github.com/leo97king/MSTL-Kace.
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Affiliation(s)
- Gang-Ao Wang
- School
of Sciences, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Xiaodi Yan
- School
of Sciences, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Xiang Li
- School
of Sciences, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Yinbo Liu
- School
of Sciences, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Junfeng Xia
- Key
Laboratory of Intelligent Computing and Signal Processing of Ministry
of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, Anhui, China
| | - Xiaolei Zhu
- School
of Sciences, Anhui Agricultural University, Hefei 230036, Anhui, China
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Tanveerul Hassan M, Tayara H, To Chong K. Meta-IL4: An Ensemble Learning Approach for IL-4-Inducing Peptide Prediction. Methods 2023:S1046-2023(23)00113-5. [PMID: 37454743 DOI: 10.1016/j.ymeth.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/25/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
The cytokine interleukin-4 (IL-4) plays an important role in our immune system. IL-4 leads the way in the differentiation of naïve T-helper 0 cells (Th0) to T-helper 2 cells (Th2). The Th2 responses are characterized by the release of IL-4. CD4+ T cells produce the cytokine IL-4 in response to exogenous parasites. IL-4 has a critical role in the growth of CD8+ cells, inflammation, and responses of T-cells. We propose an ensemble model for the prediction of IL-4 inducing peptides. Four feature encodings were extracted to build an efficient predictor: pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, quasi-sequence-order, and Shannon entropy. We developed an ensemble learning model fusion of random forest, extreme gradient boost, light gradient boosting machine, and extra tree classifier in the first layer, and a Gaussian process classifier as a meta classifier in the second layer. The outcome of the benchmarking testing dataset, with a Matthews correlation coefficient of 0.793, showed that the meta-model (Meta-IL4) outperformed individual classifiers. The highest accuracy achieved by the Meta-IL4 model is 90.70%. These findings suggest that peptides that induce IL-4 can be predicted with reasonable accuracy. These models could aid in the development of peptides that trigger the appropriate Th2 response.
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Affiliation(s)
- Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, South Korea.
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Mini-review: Recent advances in post-translational modification site prediction based on deep learning. Comput Struct Biotechnol J 2022; 20:3522-3532. [PMID: 35860402 PMCID: PMC9284371 DOI: 10.1016/j.csbj.2022.06.045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022] Open
Abstract
Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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Key Words
- AAindex, Amino acid index
- ATP, Adenosine triphosphate
- AUC, Area under curve
- Ac, Acetylation
- BE, Binary encoding
- BLOSUM, Blocks substitution matrix
- Bi-LSTM, Bidirectional LSTM
- CKSAAP, Composition of k-spaced amino acid Pairs
- CNN, Convolutional neural network
- CNNOH, CNN with the one-hot encoding
- CNNWE, CNN with the word-embedding encoding
- CNNrgb, CNN red green blue
- CV, Cross-validation
- DC-CNN, Densely connected convolutional neural network
- DL, Deep learning
- DNNs, Deep neural networks
- Deep learning
- E. coli, Escherichia coli
- EBGW, Encoding based on grouped weight
- EGAAC, Enhanced grouped amino acids content
- IG, Information gain
- K, Lysine
- KNN, k nearest neighbor
- LASSO, Least absolute shrinkage and selection operator
- LSTM, Long short-term memory
- LSTMWE, LSTM with the word-embedding encoding
- M.musculus, Mus musculus
- MDC, Modular densely connected convolutional networks
- MDCAN, Multilane dense convolutional attention network
- ML, Machine learning
- MLP, Multilayer perceptron
- MMI, Multivariate mutual information
- Machine learning
- Mass spectrometry
- NMBroto, Normalized Moreau-Broto autocorrelation
- P, Proline
- PSP, PhosphoSitePlus
- PSSM, Position-specific scoring matrix
- PTM, Post-translational modifications
- Ph, Phosphorylation
- Post-translational modification
- Prediction
- PseAAC, Pseudo-amino acid composition
- R, Arginine
- RF, Random forest
- RNN, Recurrent neural network
- ROC, Receiver operating characteristic
- S, Serine
- S. typhimurium, Salmonella typhimurium
- S.cerevisiae, Saccharomyces cerevisiae
- SE, Squeeze and excitation
- SEV, Split to Equal Validation
- ST, Source and target
- SUMO, Small ubiquitin-like modifier
- SVM, Support vector machines
- T, Threonine
- Ub, Ubiquitination
- Y, Tyrosine
- ZSL, Zero-shot learning
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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Abstract
Background:
The CRISPR system can quickly achieve the editing of different gene loci by
changing a small sequence on a single guide RNA. But the off-target event limits the further development
of the CRISPR system. How to improve the efficiency and specificity of this technology and minimize
the risk of off-target have always been a challenge. For genome-wide CRISPR Off-Target Cleavage
Sites (OTS) prediction, an important issue is data imbalance, that is, the number of true OTS identified
is much less than that of all possible nucleotide mismatch loci.
Method:
In this work, based on the sequence-generating adversarial network (SeqGAN), positive offtarget
sequences were generated to amplify the off-target gene locus OTS dataset of Cpf1. Then we
trained the data by a deep Convolutional Neural Network (CNN) to obtain a predictor with stronger
generalization ability and better performance.
Results:
In 10-fold cross-validation, the AUC value of the CNN classifier after SeqGAN balance was
0.941, which was higher than that of the original 0.863 and over-sampling 0.929. In independence testing,
the AUC value of the CNN classifier after SeqGAN balance was 0.841, which was higher than that
of the original 0.833 and over-sampling 0.836. The PR value was 0.722 after SeqGAN, which was also
about higher 0.16 than the original data and higher about 0.03 than over-sampling.
Conclusion:
The sequence generation antagonistic network SeqGAN was firstly used to deal with data
imbalance processing on CRISPR data. All the results showed that the SeqGAN can effectively generate
positive data for CRISPR off-target sites.
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Affiliation(s)
- Wen Li
- Institute of Computing Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiao-Bo Wang
- Institute of Applied Physics and Computational Mathematics, Beijing 100083, China
| | - Yan Xu
- Institute of Computing Technology, University of Science and Technology Beijing, Beijing 100083, China
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Chen Z, Liu X, Li F, Li C, Marquez-Lago T, Leier A, Webb GI, Xu D, Akutsu T, Song J. Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL. Methods Mol Biol 2022; 2499:205-219. [PMID: 35696083 DOI: 10.1007/978-1-0716-2317-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Among various types of protein post-translational modifications (PTMs), lysine PTMs play an important role in regulating a wide range of functions and biological processes. Due to the generation and accumulation of enormous amount of protein sequence data by ongoing whole-genome sequencing projects, systematic identification of different types of lysine PTM substrates and their specific PTM sites in the entire proteome is increasingly important and has therefore received much attention. Accordingly, a variety of computational methods for lysine PTM identification have been developed based on the combination of various handcrafted sequence features and machine-learning techniques. In this chapter, we first briefly review existing computational methods for lysine PTM identification and then introduce a recently developed deep learning-based method, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs). Specifically, MUscADEL employs bidirectional long short-term memory (BiLSTM) recurrent neural networks and is capable of predicting eight major types of lysine PTMs in both the human and mouse proteomes. The web server of MUscADEL is publicly available at http://muscadel.erc.monash.edu/ for the research community to use.
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Affiliation(s)
- Zhen Chen
- Key Laboratory of Rice Biology in Henan Province, Henan Agricultural University, Zhengzhou, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Chen Li
- Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Tatiana Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Dakang Xu
- Faculty of Medical Laboratory Science, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Molecular and Translational Science, Faculty of Medicine, Hudson Institute of Medical Research, Monash University, Melbourne, VIC, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
| | - Jiangning Song
- Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
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8
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Gupta R, Kumar P. CREB1 K292 and HINFP K330 as Putative Common Therapeutic Targets in Alzheimer's and Parkinson's Disease. ACS OMEGA 2021; 6:35780-35798. [PMID: 34984308 PMCID: PMC8717564 DOI: 10.1021/acsomega.1c05827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/07/2021] [Indexed: 05/16/2023]
Abstract
Integration of omics data and deciphering the mechanism of a biological regulatory network could be a promising approach to reveal the molecular mechanism involved in the progression of complex diseases, including Alzheimer's and Parkinson's. Despite having an overlapping mechanism in the etiology of Alzheimer's disease (AD) and Parkinson's disease (PD), the exact mechanism and signaling molecules behind them are still unknown. Further, the acetylation mechanism and histone deacetylase (HDAC) enzymes provide a positive direction toward studying the shared phenomenon between AD and PD pathogenesis. For instance, increased expression of HDACs causes a decrease in protein acetylation status, resulting in decreased cognitive and memory function. Herein, we employed an integrative approach to analyze the transcriptomics data that established a potential relationship between AD and PD. Data preprocessing and analysis of four publicly available microarray datasets revealed 10 HUB proteins, namely, CDC42, CD44, FGFR1, MYO5A, NUMA1, TUBB4B, ARHGEF9, USP5, INPP5D, and NUP93, that may be involved in the shared mechanism of AD and PD pathogenesis. Further, we identified the relationship between the HUB proteins and transcription factors that could be involved in the overlapping mechanism of AD and PD. CREB1 and HINFP were the crucial regulatory transcription factors that were involved in the AD and PD crosstalk. Further, lysine acetylation sites and HDAC enzyme prediction revealed the involvement of 15 and 27 potential lysine residues of CREB1 and HINFP, respectively. Our results highlighted the importance of HDAC1(K292) and HDAC6(K330) association with CREB1 and HINFP, respectively, in the AD and PD crosstalk. However, different datasets with a large number of samples and wet lab experimentation are required to validate and pinpoint the exact role of CREB1 and HINFP in the AD and PD crosstalk. It is also possible that the different datasets may or may not affect the results due to analysis parameters. In conclusion, our study potentially highlighted the crucial proteins, transcription factors, biological pathways, lysine residues, and HDAC enzymes shared between AD and PD at the molecular level. The findings can be used to study molecular studies to identify the possible relationship in the AD-PD crosstalk.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and
Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Delhi 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and
Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Delhi 110042, India
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Yu K, Zhang Q, Liu Z, Du Y, Gao X, Zhao Q, Cheng H, Li X, Liu ZX. Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation. Brief Bioinform 2021; 21:1798-1805. [PMID: 32978618 DOI: 10.1093/bib/bbz107] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/18/2019] [Accepted: 07/30/2019] [Indexed: 11/14/2022] Open
Abstract
Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein-protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.
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Affiliation(s)
- Kai Yu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zekun Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yimeng Du
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xinjiao Gao
- Division of Molecular and Cell Biophysics, Hefei National Science Center for Physical Sciences at the Microscale, Anhui Key Laboratory of Cellular Dynamics and Chemical Biology, School of Life Sciences, University of Science and Technology of the China, Hefei 230027, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Han Cheng
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaoxing Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ze-Xian Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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10
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Basith S, Lee G, Manavalan B. STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform 2021; 23:6370848. [PMID: 34532736 PMCID: PMC8769686 DOI: 10.1093/bib/bbab376] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea
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11
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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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12
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Li P, Chen C, Li P, Dong Y. A comprehensive examination of the lysine acetylation targets in paper mulberry based on proteomics analyses. PLoS One 2021; 16:e0240947. [PMID: 33705403 PMCID: PMC7951917 DOI: 10.1371/journal.pone.0240947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/12/2021] [Indexed: 11/19/2022] Open
Abstract
Rocky desertification is a bottleneck that reduces ecological and environmental security in karst areas. Paper mulberry, a unique deciduous tree, shows good performance in rocky desertification areas. Its resistance mechanisms are therefore of high interest. In this study, a lysine acetylation proteomics analysis of paper mulberry seedling leaves was conducted in combination with the purification of acetylated protein by high-precision nano LC-MS/MS. We identified a total of 7130 acetylation sites in 3179 proteins. Analysis of the modified sites showed a predominance of nine motifs. Six positively charged residues: lysine (K), arginine (R), and histidine (H), serine (S), threonine (T), and tyrosine (Y) occurred most frequently at the +1 position, phenylalanine (F) was both detected both upstream and downstream of the acetylated lysines; and the sequence logos showed a strong preference for lysine and arginine around acetylated lysines. Functional annotation revealed that the identified enzymes were mainly involved in translation, transcription, ribosomal structure and biological processes, showing that lysine acetylation can regulate various aspects of primary carbon and nitrogen metabolism and secondary metabolism. Acetylated proteins were enriched in the chloroplast, cytoplasm, and nucleus, and many stress response-related proteins were also discovered to be acetylated, including PAL, HSP70, and ERF. HSP70, an important protein involved in plant abiotic and disease stress responses, was identified in paper mulberry, although it is rarely found in woody plants. This may be further examined in research in other plants and could explain the good adaptation of paper mulberry to the karst environment. However, these hypotheses require further verification. Our data can provide a new starting point for the further analysis of the acetylation function in paper mulberry and other plants.
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Affiliation(s)
- Ping Li
- College of Animal Science, Guizhou university, Guiyang, Guizhou, China
| | - Chao Chen
- College of Animal Science, Guizhou university, Guiyang, Guizhou, China
| | - Ping Li
- Institute of Grassland Research, Sichuan Academy of Grassland Science, Cheng Du, Si Chuan, China
| | - Yibo Dong
- College of Animal Science, Guizhou university, Guiyang, Guizhou, China
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13
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Ao C, Yu L, Zou Q. Prediction of bio-sequence modifications and the associations with diseases. Brief Funct Genomics 2020; 20:1-18. [PMID: 33313647 DOI: 10.1093/bfgp/elaa023] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022] Open
Abstract
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
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14
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Zheng W, Wuyun Q, Cheng M, Hu G, Zhang Y. Two-Level Protein Methylation Prediction using structure model-based features. Sci Rep 2020; 10:6008. [PMID: 32265459 PMCID: PMC7138832 DOI: 10.1038/s41598-020-62883-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 03/16/2020] [Indexed: 01/26/2023] Open
Abstract
Protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.
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Affiliation(s)
- Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China
| | - Qiqige Wuyun
- Computer Science and Engineering Department, Michigan State University, East Lansing, MI, 48823, USA.,School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China
| | - Micah Cheng
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China.
| | - Yanping Zhang
- Department of Mathematics, School of Mathematics and Physics, Hebei University of Engineering, Handan, 056038, PR China.
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15
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Chen Z, Liu X, Li F, Li C, Marquez-Lago T, Leier A, Akutsu T, Webb GI, Xu D, Smith AI, Li L, Chou KC, Song J. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform 2019; 20:2267-2290. [PMID: 30285084 PMCID: PMC6954452 DOI: 10.1093/bib/bby089] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 08/17/2018] [Accepted: 08/18/2018] [Indexed: 12/22/2022] Open
Abstract
Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.
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Affiliation(s)
- Zhen Chen
- School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China
| | - Xuhan Liu
- Medicinal Chemistry, Leiden Academic Centre for Drug Research,Einsteinweg, Leiden, The Netherlands
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Institute of Molecular Systems Biology, ETH Zürich,Auguste-Piccard-Hof, Zürich, Switzerland
| | - Tatiana Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research,Kyoto University, Uji, Kyoto, Japan
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Dakang Xu
- Faculty of Medical Laboratory Science, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Molecular and Translational Science, Faculty of Medicine, Hudson Institute of Medical Research, Monash University, Melbourne, VIC, Australia
| | - Alexander Ian Smith
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Lei Li
- School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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16
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Deacetylation of metabolic enzymes by Sirt2 modulates pyruvate homeostasis to extend insect lifespan. Aging (Albany NY) 2019; 10:1053-1072. [PMID: 29769432 PMCID: PMC5990394 DOI: 10.18632/aging.101447] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 05/08/2018] [Indexed: 12/13/2022]
Abstract
Diapause in insects is akin to dauer in Caenorhabditis elegans and hibernation in vertebrates. Diapause causes a profound extension of lifespan by low metabolic activity. However, the detailed regulatory mechanisms for low metabolic activity remain unknown. Here, we showed that low pyruvate levels are present in the brains of diapause-destined pupae of the cotton bollworm Helicoverpa armigera, and three enzymes pyruvate kinase (PK), phosphoenolpyruvate carboxykinase (PEPCK), and phosphoglycerate mutase (PGAM) are closely correlated with pyruvate homeostasis. Notably, Sirt2 can deacetylate the three enzymes to increase their activity in vitro. Thus, low Sirt2 expression in the brains of diapause individuals decreases PK and PEPCK protein levels as well as PGAM activity, resulting in low pyruvate levels and low tricarboxylic acid cycle activity and eventually inducing diapause initiation by low metabolic activity. These findings suggest that pyruvate is a checkpoint for development or lifespan extension, and Sirt2 is a negative regulator to extend lifespan in insects.
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17
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Lu C, Liu Z, Zhang E, He F, Ma Z, Wang H. MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models. Int J Mol Sci 2019; 20:ijms20133120. [PMID: 31247932 PMCID: PMC6651575 DOI: 10.3390/ijms20133120] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/23/2019] [Accepted: 06/23/2019] [Indexed: 02/07/2023] Open
Abstract
Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target MPs. Therefore, accurately identifying the membrane protein-ligand binding sites (MPLs) will significantly improve drug discovery. In this paper, we propose a sequence-based MPLs predictor called MPLs-Pred, where evolutionary profiles, topology structure, physicochemical properties, and primary sequence segment descriptors are combined as features applied to a random forest classifier, and an under-sampling scheme is used to enhance the classification capability with imbalanced samples. Additional ligand-specific models were taken into consideration in refining the prediction. The corresponding experimental results based on our method achieved an appreciable performance, with 0.63 MCC (Matthews correlation coefficient) as the overall prediction precision, and those values were 0.604, 0.7, and 0.692, respectively, for the three main types of ligands: drugs, metal ions, and biomacromolecules. MPLs-Pred is freely accessible at http://icdtools.nenu.edu.cn/.
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Affiliation(s)
- Chang Lu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Zhe Liu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Enju Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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18
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Moruno Algara M, Kuczyńska‐Wiśnik D, Dębski J, Stojowska‐Swędrzyńska K, Sominka H, Bukrejewska M, Laskowska E. Trehalose protects
Escherichia coli
against carbon stress manifested by protein acetylation and aggregation. Mol Microbiol 2019; 112:866-880. [DOI: 10.1111/mmi.14322] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2019] [Indexed: 12/22/2022]
Affiliation(s)
- María Moruno Algara
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
| | - Dorota Kuczyńska‐Wiśnik
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
| | - Janusz Dębski
- Mass Spectrometry Laboratory IBB PAS ul. Pawińskiego 5A02‐106Warsaw Poland
| | - Karolina Stojowska‐Swędrzyńska
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
| | - Hanna Sominka
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
| | - Małgorzata Bukrejewska
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
| | - Ewa Laskowska
- Faculty of Biology, Department of General and Medical Biochemistry University of Gdansk Wita Stwosza 5980‐308Gdansk Poland
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19
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Ning Q, Yu M, Ji J, Ma Z, Zhao X. Analysis and prediction of human acetylation using a cascade classifier based on support vector machine. BMC Bioinformatics 2019; 20:346. [PMID: 31208321 PMCID: PMC6580503 DOI: 10.1186/s12859-019-2938-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 06/06/2019] [Indexed: 12/24/2022] Open
Abstract
Background Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information. Results The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier. Conclusions In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data.
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Affiliation(s)
- Qiao Ning
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Miao Yu
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Jinchao Ji
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
| | - Xiaowei Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
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20
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Ning Q, Ma Z, Zhao X. dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components. J Theor Biol 2019; 470:43-49. [DOI: 10.1016/j.jtbi.2019.03.011] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 03/09/2019] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
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21
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Xu Y, Yang Y, Wang Z, Shao Y. Prediction of Acetylation and Succinylation in Proteins Based on Multilabel Learning RankSVM. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180830101540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In vivo, one of the most efficient biological mechanisms for expanding the genetic code and regulating cellular physiology is protein post-translational modification (PTM). Because PTM can provide very useful information for both basic research and drug development, identification of PTM sites in proteins has become a very important topic in bioinformatics. Lysine residue in protein can be subjected to many types of PTMs, such as acetylation, succinylation, methylation and propionylation and so on. In order to deal with the huge protein sequences, the present study is devoted to developing computational techniques that can be used to predict the multiple K-type modifications of any uncharacterized protein timely and effectively. In this work, we proposed a method which could deal with the acetylation and succinylation prediction in a multilabel learning. Three feature constructions including sequences and physicochemical properties have been applied. The multilabel learning algorithm RankSVM has been first used in PTMs. In 10-fold cross-validation the predictor with physicochemical properties encoding got accuracy 73.86%, abslute-true 64.70%, respectively. They were better than the other feature constructions. We compared with other multilabel algorithms and the existing predictor iPTM-Lys. The results of our predictor were better than other methods. Meanwhile we also analyzed the acetylation and succinylation peptides which could illustrate the results.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Zu Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yuanhai Shao
- School of Economics and Management, Hainan University, Haikou 570228, China
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22
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Wu M, Yang Y, Wang H, Xu Y. A deep learning method to more accurately recall known lysine acetylation sites. BMC Bioinformatics 2019; 20:49. [PMID: 30674277 PMCID: PMC6343287 DOI: 10.1186/s12859-019-2632-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/16/2019] [Indexed: 12/11/2022] Open
Abstract
Background Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data. Results In this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC. Conclusion The predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet. Electronic supplementary material The online version of this article (10.1186/s12859-019-2632-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meiqi Wu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China. .,Beijing Key Laboratory for Magneto-photoelectrical Composite and Interface Science, University of Science and Technology Beijing, Beijing, 100083, China.
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23
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Chen G, Cao M, Yu J, Guo X, Shi S. Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC. J Theor Biol 2019; 461:92-101. [DOI: 10.1016/j.jtbi.2018.10.047] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 10/09/2018] [Accepted: 10/22/2018] [Indexed: 12/12/2022]
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24
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Bao W, Yang B, Li Z, Zhou Y. LAIPT: Lysine Acetylation Site Identification with Polynomial Tree. Int J Mol Sci 2018; 20:ijms20010113. [PMID: 30597947 PMCID: PMC6337602 DOI: 10.3390/ijms20010113] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 11/30/2018] [Accepted: 12/05/2018] [Indexed: 11/16/2022] Open
Abstract
Post-translational modification plays a key role in the field of biology. Experimental identification methods are time-consuming and expensive. Therefore, computational methods to deal with such issues overcome these shortcomings and limitations. In this article, we propose a lysine acetylation site identification with polynomial tree method (LAIPT), making use of the polynomial style to demonstrate amino-acid residue relationships in peptide segments. This polynomial style was enriched by the physical and chemical properties of amino-acid residues. Then, these reconstructed features were input into the employed classification model, named the flexible neural tree. Finally, some effect evaluation measurements were employed to test the model’s performance.
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Affiliation(s)
- Wenzheng Bao
- School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China.
| | - Bin Yang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Zhengwei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
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25
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Cao M, Chen G, Yu J, Shi S. Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy. Brief Bioinform 2018; 21:595-608. [DOI: 10.1093/bib/bby122] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 11/16/2018] [Accepted: 11/22/2018] [Indexed: 11/12/2022] Open
Abstract
Abstract
Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.
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Affiliation(s)
- Man Cao
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Guodong Chen
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Jialin Yu
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Shaoping Shi
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
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26
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Ning Q, Zhao X, Bao L, Ma Z, Zhao X. Detecting Succinylation sites from protein sequences using ensemble support vector machine. BMC Bioinformatics 2018; 19:237. [PMID: 29940836 PMCID: PMC6016146 DOI: 10.1186/s12859-018-2249-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 06/14/2018] [Indexed: 12/14/2022] Open
Abstract
Background Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. Results The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. Conclusions The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available athttps://github.com/ningq669/PSuccE. Electronic supplementary material The online version of this article (10.1186/s12859-018-2249-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qiao Ning
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Xiaosa Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Lingling Bao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
| | - Xiaowei Zhao
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China.
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27
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Ju Z, He JJ. Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection. Anal Biochem 2018; 550:1-7. [DOI: 10.1016/j.ab.2018.04.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/05/2018] [Accepted: 04/06/2018] [Indexed: 12/17/2022]
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28
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Chen G, Cao M, Luo K, Wang L, Wen P, Shi S. ProAcePred: prokaryote lysine acetylation sites prediction based on elastic net feature optimization. Bioinformatics 2018; 34:3999-4006. [DOI: 10.1093/bioinformatics/bty444] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 05/30/2018] [Indexed: 02/02/2023] Open
Affiliation(s)
- Guodong Chen
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Man Cao
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Kun Luo
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Lina Wang
- Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China
| | - Pingping Wen
- Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China
| | - Shaoping Shi
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
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29
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Ahmed MS, Shahjaman M, Kabir E, Kamruzzaman M. Prediction of Protein Acetylation Sites using Kernel Naive Bayes Classifier Based on Protein Sequences Profiling. Bioinformation 2018; 14:213-218. [PMID: 30108418 PMCID: PMC6077816 DOI: 10.6026/97320630014213] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/29/2018] [Accepted: 04/29/2018] [Indexed: 12/11/2022] Open
Abstract
Lysine acetylation is one of the decisive categories of protein post-translational modification (PTM), it is convoluted in many significant cellular developments and severe diseases in the biological system. The experimental identification of protein-acetylated sites is painstaking, time-consuming and expensive. Hence, there is significant interest in the development of computational approaches for consistent prediction of acetylation sites using protein sequences. Features selection from protein sequences plays a significant role for acetylation sites prediction. We describe an improved feature selection approach for acetylation sites prediction based on kernel naive Bayes classifier (KNBC). We have shown that KNBC generated from selected features by a new feature selection method outperforms than the existing methods for identification of acetylation sites. The sensitivity, specificity, ACC (Accuracy), MCC (Matthews Correlation Coefficient) and AUC (Area under Curve of ROC) in our proposed method are as follows 80.71%, 93.39%, 76.73%, 41.37% and 83.0% with the optimum window size is 47. Thus the kernel naive Bayes classifier finds application in acetylation site prediction.
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Affiliation(s)
- Md. Shakil Ahmed
- Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh
| | - Enamul Kabir
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia
| | - Md. Kamruzzaman
- Data Science for Knowledge Creation Research Center, Seoul National University, Korea
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30
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Xu HD, Wang LN, Wen PP, Shi SP, Qiu JD. Site-Specific Systematic Analysis of Lysine Modification Crosstalk. Proteomics 2018. [DOI: 10.1002/pmic.201700292] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Hao-Dong Xu
- Department of Chemistry; Nanchang University; No. 999 Xuefu Road Nanchang Honggutan New District Jiangxi Province 330031 P. R. China
| | - Li-Na Wang
- Department of Chemistry; Nanchang University; No. 999 Xuefu Road Nanchang Honggutan New District Jiangxi Province 330031 P. R. China
| | - Ping-Ping Wen
- Department of Chemistry; Nanchang University; No. 999 Xuefu Road Nanchang Honggutan New District Jiangxi Province 330031 P. R. China
| | - Shao-Ping Shi
- Department of Chemistry; Nanchang University; No. 999 Xuefu Road Nanchang Honggutan New District Jiangxi Province 330031 P. R. China
| | - Jian-Ding Qiu
- Department of Chemistry; Nanchang University; No. 999 Xuefu Road Nanchang Honggutan New District Jiangxi Province 330031 P. R. China
- Department of Materials and Chemical Engineering; Pingxiang University; Pingxiang P. R. China
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31
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Wang LN, Shi SP, Wen PP, Zhou ZY, Qiu JD. Computing Prediction and Functional Analysis of Prokaryotic Propionylation. J Chem Inf Model 2017; 57:2896-2904. [DOI: 10.1021/acs.jcim.7b00482] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Li-Na Wang
- College
of Chemistry and Institute for Advanced Study, Nanchang University, Nanchang 330031, China
- Department
of Sciences, Nanchang Institute of Technology, Nanchang 330099, China
| | - Shao-Ping Shi
- College
of Chemistry and Institute for Advanced Study, Nanchang University, Nanchang 330031, China
| | - Ping-Ping Wen
- College
of Chemistry and Institute for Advanced Study, Nanchang University, Nanchang 330031, China
| | - Zhi-You Zhou
- College
of Chemistry and Institute for Advanced Study, Nanchang University, Nanchang 330031, China
| | - Jian-Ding Qiu
- College
of Chemistry and Institute for Advanced Study, Nanchang University, Nanchang 330031, China
- Department
of Materials and Chemical Engineering, Pingxiang University, Pingxiang 337055, China
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32
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Audagnotto M, Dal Peraro M. Protein post-translational modifications: In silico prediction tools and molecular modeling. Comput Struct Biotechnol J 2017; 15:307-319. [PMID: 28458782 PMCID: PMC5397102 DOI: 10.1016/j.csbj.2017.03.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Revised: 03/17/2017] [Accepted: 03/21/2017] [Indexed: 02/09/2023] Open
Abstract
Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.
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Affiliation(s)
- Martina Audagnotto
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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33
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Charles S, Raj V, Arokiaraj J, Mala K. Caveolin1/protein arginine methyltransferase1/sirtuin1 axis as a potential target against endothelial dysfunction. Pharmacol Res 2017; 119:1-11. [PMID: 28126510 DOI: 10.1016/j.phrs.2017.01.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 11/20/2016] [Accepted: 01/22/2017] [Indexed: 12/23/2022]
Abstract
Endothelial dysfunction (ED), an established response to cardiovascular risk factors, is characterized by increased levels of soluble molecules secreted by endothelial cells (EC). Evidence suggest that ED is an independent predictor of cardiac events and that it is associated with a deficiency in production or bioavailability of nitric oxide (NO) and/or an imbalance in the relative contribution of endothelium-derived relaxing and contracting factors. ED can be reversed by treating cardiovascular risk factors, hence, beyond ambiguity, ED contributes to initiation and progression of atherosclerotic disease. Majority of cardiovascular risk factors act by a common pathway, oxidative stress (OS), characterized by an imbalance in bioavailability of NO and reactive oxygen species (ROS). Enhanced ROS, through several mechanisms, alters competence of EC that leads to ED, reducing its potential to maintain homeostasis and resulting in development of cardiovascular disease (CVD). Influential mechanisms that have been implicated in the development of ED include (i) presence of elevated levels of NOS inhibitor, asymmetric dimethylarginine (ADMA) due to augmented enzyme activity of protein arginine methyl transferase-1 (PRMT1); (ii) decrease in NO generation by endothelial nitric oxide synthase (eNOS) uncoupling, or by reaction of NO with free radicals and (iii) impaired post translational modification of protein (PTM) such as eNOS, caveolin-1 (cav1) and sirtuin-1 (SIRT1). However, the inter-related mechanisms that concur to developing ED is yet to be understood. The events that possibly overlay include OS-induced sequestration of SIRT1 to caveolae facilitating cav1-SIRT1 association; potential increase in lysine acetylation of enzymes such as eNOS and PRMT1 leading to enhanced ADMA formation; imbalance in acetylation-methylation ratio (AMR); diminished NO generation and ED. Here we review current literature from research showing interdependent association between cav1-PRMT1-SIRT1 to the outcomes of experimental and clinical research aiming to preserve endothelial function with gene- or pharmaco-therapy.
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Affiliation(s)
- Soniya Charles
- Department of Biotechnology, School of Bioengineering, SRM University, Potheri 603203, Tamil Nadu, India
| | - Vijay Raj
- Medical College Hospital and Research Center, SRM University, Potheri 603203, Tamil Nadu, India
| | - Jesu Arokiaraj
- Department of Biotechnology, Faculty of Science and Humanities, SRM University, Potheri 603203, Tamil Nadu, India
| | - Kanchana Mala
- Medical College Hospital and Research Center, SRM University, Potheri 603203, Tamil Nadu, India.
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34
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Patel K, Singh M, Gowda H. Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data. Methods Mol Biol 2017; 1549:147-161. [PMID: 27975290 DOI: 10.1007/978-1-4939-6740-7_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-throughput proteomics studies generate large amounts of data. Biological interpretation of these large scale datasets is often challenging. Over the years, several computational tools have been developed to facilitate meaningful interpretation of large-scale proteomics data. In this chapter, we describe various analyses that can be performed and bioinformatics tools and resources that enable users to do the analyses. Many Web-based and stand-alone tools are relatively user-friendly and can be used by most biologists without significant assistance.
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Affiliation(s)
- Krishna Patel
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Manika Singh
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India
| | - Harsha Gowda
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India.
- YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore, India.
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35
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GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences. Sci Rep 2016; 6:39787. [PMID: 28004786 PMCID: PMC5177928 DOI: 10.1038/srep39787] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 11/28/2016] [Indexed: 01/02/2023] Open
Abstract
Protein acetylation catalyzed by specific histone acetyltransferases (HATs) is an essential post-translational modification (PTM) and involved in the regulation a broad spectrum of biological processes in eukaryotes. Although several ten thousands of acetylation sites have been experimentally identified, the upstream HATs for most of the sites are unclear. Thus, the identification of HAT-specific acetylation sites is fundamental for understanding the regulatory mechanisms of protein acetylation. In this work, we first collected 702 known HAT-specific acetylation sites of 205 proteins from the literature and public data resources, and a motif-based analysis demonstrated that different types of HATs exhibit similar but considerably distinct sequence preferences for substrate recognition. Using 544 human HAT-specific sites for training, we constructed a highly useful tool of GPS-PAIL for the prediction of HAT-specific sites for up to seven HATs, including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8. The prediction accuracy of GPS-PAIL was critically evaluated, with a satisfying performance. Using GPS-PAIL, we also performed a large-scale prediction of potential HATs for known acetylation sites identified from high-throughput experiments in nine eukaryotes. Both online service and local packages were implemented, and GPS-PAIL is freely available at: http://pail.biocuckoo.org.
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36
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Fournier M, Orpinell M, Grauffel C, Scheer E, Garnier JM, Ye T, Chavant V, Joint M, Esashi F, Dejaegere A, Gönczy P, Tora L. KAT2A/KAT2B-targeted acetylome reveals a role for PLK4 acetylation in preventing centrosome amplification. Nat Commun 2016; 7:13227. [PMID: 27796307 PMCID: PMC5095585 DOI: 10.1038/ncomms13227] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 09/14/2016] [Indexed: 12/12/2022] Open
Abstract
Lysine acetylation is a widespread post-translational modification regulating various biological processes. To characterize cellular functions of the human lysine acetyltransferases KAT2A (GCN5) and KAT2B (PCAF), we determined their acetylome by shotgun proteomics. One of the newly identified KAT2A/2B substrate is polo-like kinase 4 (PLK4), a key regulator of centrosome duplication. We demonstrate that KAT2A/2B acetylate the PLK4 kinase domain on residues K45 and K46. Molecular dynamics modelling suggests that K45/K46 acetylation impairs kinase activity by shifting the kinase to an inactive conformation. Accordingly, PLK4 activity is reduced upon in vitro acetylation of its kinase domain. Moreover, the overexpression of the PLK4 K45R/K46R mutant in cells does not lead to centrosome overamplification, as observed with wild-type PLK4. We also find that impairing KAT2A/2B-acetyltransferase activity results in diminished phosphorylation of PLK4 and in excess centrosome numbers in cells. Overall, our study identifies the global human KAT2A/2B acetylome and uncovers that KAT2A/2B acetylation of PLK4 prevents centrosome amplification. The acetyltransferases KAT2A and KAT2B are essential regulators of transcription, cell cycle progression and DNA repair. Here the authors describe a KAT2A/2B-dependent acetylome, and show that acetylation of the protein kinase PLK4 contributes to the regulation of centrosome number.
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Affiliation(s)
- Marjorie Fournier
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France.,Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Meritxell Orpinell
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Cédric Grauffel
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Elisabeth Scheer
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Jean-Marie Garnier
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Tao Ye
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Virginie Chavant
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Mathilde Joint
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Fumiko Esashi
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Annick Dejaegere
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
| | - Pierre Gönczy
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - László Tora
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404 Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, 67404 Illkirch, France.,Université de Strasbourg, 67404 Illkirch, France
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37
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Phosphorylation of the Brome Mosaic Virus Capsid Regulates the Timing of Viral Infection. J Virol 2016; 90:7748-60. [PMID: 27334588 DOI: 10.1128/jvi.00833-16] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 06/10/2016] [Indexed: 11/20/2022] Open
Abstract
UNLABELLED The four brome mosaic virus (BMV) RNAs (RNA1 to RNA4) are encapsidated in three distinct virions that have different disassembly rates in infection. The mechanism for the differential release of BMV RNAs from virions is unknown, since 180 copies of the same coat protein (CP) encapsidate each of the BMV genomic RNAs. Using mass spectrometry, we found that the BMV CP contains a complex pattern of posttranslational modifications. Treatment with phosphatase was found to not significantly affect the stability of the virions containing RNA1 but significantly impacted the stability of the virions that encapsidated BMV RNA2 and RNA3/4. Cryo-electron microscopy reconstruction revealed dramatic structural changes in the capsid and the encapsidated RNA. A phosphomimetic mutation in the flexible N-terminal arm of the CP increased BMV RNA replication and virion production. The degree of phosphorylation modulated the interaction of CP with the encapsidated RNA and the release of three of the BMV RNAs. UV cross-linking and immunoprecipitation methods coupled to high-throughput sequencing experiments showed that phosphorylation of the BMV CP can impact binding to RNAs in the virions, including sequences that contain regulatory motifs for BMV RNA gene expression and replication. Phosphatase-treated virions affected the timing of CP expression and viral RNA replication in plants. The degree of phosphorylation decreased when the plant hosts were grown at an elevated temperature. These results show that phosphorylation of the capsid modulates BMV infection. IMPORTANCE How icosahedral viruses regulate the release of viral RNA into the host is not well understood. The selective release of viral RNA can regulate the timing of replication and gene expression. Brome mosaic virus (BMV) is an RNA virus, and its three genomic RNAs are encapsidated in separate virions. Through proteomic, structural, and biochemical analyses, this work shows that posttranslational modifications, specifically, phosphorylation, on the capsid protein regulate the capsid-RNA interaction and the stability of the virions and affect viral gene expression. Mutational analysis confirmed that changes in modification affected virion stability and the timing of viral infection. The mechanism for modification of the virion has striking parallels to the mechanism of regulation of chromatin packaging by nucleosomes.
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38
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Wen PP, Shi SP, Xu HD, Wang LN, Qiu JD. Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization. Bioinformatics 2016; 32:3107-3115. [PMID: 27354692 DOI: 10.1093/bioinformatics/btw377] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2016] [Accepted: 06/13/2016] [Indexed: 02/04/2023] Open
Abstract
As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. AVAILABILITY AND IMPLEMENTATION The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ping-Ping Wen
- Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China
| | - Shao-Ping Shi
- Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China
| | - Hao-Dong Xu
- Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China
| | - Li-Na Wang
- Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China
| | - Jian-Ding Qiu
- Department of Chemistry, Department of Mathematics, Nanchang University, Nanchang 330031, China Department of Materials and Chemical Engineering, Pingxiang University, Pingxiang 337055, China
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39
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Karabulut NP, Frishman D. Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites. PLoS One 2016; 11:e0157896. [PMID: 27332813 PMCID: PMC4917084 DOI: 10.1371/journal.pone.0157896] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 06/07/2016] [Indexed: 01/22/2023] Open
Abstract
Phosphorylation is the most widespread and well studied reversible posttranslational modification. Discovering tissue-specific preferences of phosphorylation sites is important as phosphorylation plays a role in regulating almost every cellular activity and disease state. Here we present a comprehensive analysis of global and tissue-specific sequence and structure properties of phosphorylation sites utilizing recent proteomics data. We identified tissue-specific motifs in both sequence and spatial environments of phosphorylation sites. Target site preferences of kinases across tissues indicate that, while many kinases mediate phosphorylation in all tissues, there are also kinases that exhibit more tissue-specific preferences which, notably, are not caused by tissue-specific kinase expression. We also demonstrate that many metabolic pathways are differentially regulated by phosphorylation in different tissues.
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Affiliation(s)
- Nermin Pinar Karabulut
- Department of Genome Oriented Bioinformatics, Technische Universität München, Freising, Germany
| | - Dmitrij Frishman
- Department of Genome Oriented Bioinformatics, Technische Universität München, Freising, Germany
- Helmholtz Zentrum Munich; German Research Center for Environmental Health (GmbH), Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
- St Petersburg State Polytechnical University, St Petersburg, Russia
- * E-mail:
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40
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Wuyun Q, Zheng W, Zhang Y, Ruan J, Hu G. Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. PLoS One 2016; 11:e0155370. [PMID: 27183223 PMCID: PMC4868276 DOI: 10.1371/journal.pone.0155370] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 04/27/2016] [Indexed: 12/21/2022] Open
Abstract
Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.
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Affiliation(s)
- Qiqige Wuyun
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
| | - Wei Zheng
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
| | - Yanping Zhang
- Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China, 056038
| | - Jishou Ruan
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China, 300071
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
- * E-mail:
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41
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A homology-based pipeline for global prediction of post-translational modification sites. Sci Rep 2016; 6:25801. [PMID: 27174170 PMCID: PMC4865729 DOI: 10.1038/srep25801] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 04/21/2016] [Indexed: 12/22/2022] Open
Abstract
The pathways of protein post-translational modifications (PTMs) have been shown to play particularly important roles for almost any biological process. Identification of PTM substrates along with information on the exact sites is fundamental for fully understanding or controlling biological processes. Alternative computational strategies would help to annotate PTMs in a high-throughput manner. Traditional algorithms are suited for identifying the common organisms and tissues that have a complete PTM atlas or extensive experimental data. While annotation of rare PTMs in most organisms is a clear challenge. In this work, to this end we have developed a novel homology-based pipeline named PTMProber that allows identification of potential modification sites for most of the proteomes lacking PTMs data. Cross-promotion E-value (CPE) as stringent benchmark has been used in our pipeline to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions. In addition, we developed a web-based tool to integrate this pipeline at http://bioinfo.ncu.edu.cn/PTMProber/index.aspx. In addition to pre-constructed prediction models of PTM, the website provides an extensional functionality to allow users to customize models.
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42
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Aftabi Y, Colagar AH, Mehrnejad F. An in silico approach to investigate the source of the controversial interpretations about the phenotypic results of the human AhR-gene G1661A polymorphism. J Theor Biol 2016; 393:1-15. [PMID: 26776670 DOI: 10.1016/j.jtbi.2016.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Revised: 12/11/2015] [Accepted: 01/01/2016] [Indexed: 12/21/2022]
Abstract
Aryl hydrocarbon receptor (AhR) acts as an enhancer binding ligand-activated intracellular receptor. Chromatin remodeling components and general transcription factors such as TATA-binding protein (TBP) are evoked on AhR-target genes by interaction with its flexible transactivation domain (TAD). AhR-G1661A single nucleotide polymorphism (SNP: rs2066853) causes an arginine to lysine substitution in the acidic sub-domain of TAD at position 554 (R554K). Although, numerous studies associate the SNP with some abnormalities such as cancer, other reliable investigations refuse the associations. Consequently, the interpretation of the phenotypic results of G1661A-transition has been controversial. In this study, an in silico analysis were performed to investigate the possible effects of the transition on AhR-mRNA, protein structure, interaction properties and modifications. The analysis revealed that the R554K substitution affects secondary structure and solvent accessibility of adjacent residues. Also, it causes to decreasing of the AhR stability; altering the hydropathy features of the local sequence and changing the pattern of the residues at the binding site of the TAD-acidic sub-domain. Generating of new sites for ubiquitination and acetylation for AhR-K554 variant respectively at positions 544 and 560 was predicted. Our findings intensify the idea that the AhR-G1661A transition may affects AhR-TAD interactions, especially with the TBP, which influence AhR-target genes expression. However, the previously reported flexibility of the modular TAD could act as an intervening factor, moderate the SNP effects and causes distinct outcomes in different individuals and tissues.
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Affiliation(s)
- Younes Aftabi
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran
| | - Abasalt Hosseinzadeh Colagar
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran.
| | - Faramarz Mehrnejad
- Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, P.O. Box: 14395-1561, Tehran, Iran
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Gianazza E, Parravicini C, Primi R, Miller I, Eberini I. In silico prediction and characterization of protein post-translational modifications. J Proteomics 2015; 134:65-75. [PMID: 26436211 DOI: 10.1016/j.jprot.2015.09.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 07/17/2015] [Accepted: 09/23/2015] [Indexed: 01/06/2023]
Abstract
This review outlines the computational approaches and procedures for predicting post translational modification (PTM)-induced changes in protein conformation and their influence on protein function(s), the latter being assessed as differential affinity in interaction with either low (ligands for receptors or transporters, substrates for enzymes) or high molecular mass molecules (proteins or nucleic acids in supramolecular assemblies). The scope for an in silico approach is discussed against a summary of the in vitro evidence on the structural and functional outcome of protein PTM.
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Affiliation(s)
- Elisabetta Gianazza
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Gruppo di Studio per la Proteomica e la Struttura delle Proteine, Sezione di Scienze Farmacologiche, Via Balzaretti 9, I-20133 Milan, Italy.
| | - Chiara Parravicini
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
| | - Roberto Primi
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
| | - Ingrid Miller
- Institut für Medizinische Biochemie, Veterinärmedizinische Universität Wien, Veterinärplatz 1, A-1210 Vienna, Austria
| | - Ivano Eberini
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
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44
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Yang R, Zhang C, Gao R, Zhang L. An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors. Int J Mol Sci 2015; 16:21191-214. [PMID: 26370959 PMCID: PMC4613249 DOI: 10.3390/ijms160921191] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 08/18/2015] [Accepted: 08/26/2015] [Indexed: 12/03/2022] Open
Abstract
Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.
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Affiliation(s)
- Runtao Yang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China.
| | - Chengjin Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China.
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China.
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan 250061, China.
| | - Lina Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, China.
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Timucin AC, Bodur C, Basaga H. SIRT1 contributes to aldose reductase expression through modulating NFAT5 under osmotic stress: In vitro and in silico insights. Cell Signal 2015; 27:2160-72. [PMID: 26297866 DOI: 10.1016/j.cellsig.2015.08.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 08/18/2015] [Indexed: 12/13/2022]
Abstract
So far, a myriad of molecules were characterized to modulate NFAT5 and its downstream targets. Among these NFAT5 modifiers, SIRT1 was proposed to have a promising role in NFAT5 dependent events, yet the exact underlying mechanism still remains obscure. Hence, the link between SIRT1 and NFAT5-aldose reductase (AR) axis under osmotic stress, was aimed to be delineated in this study. A unique osmotic stress model was generated and its mechanistic components were deciphered in U937 monocytes. In this model, AR expression and nuclear NFAT5 stabilization were revealed to be positively regulated by SIRT1 through utilization of pharmacological modulators. Overexpression and co-transfection studies of NFAT5 and SIRT1 further validated the contribution of SIRT1 to AR and NFAT5. The involvement of SIRT1 activity in these events was mediated via modification of DNA binding of NFAT5 to AR ORE region. Besides, NFAT5 and SIRT1 were also shown to co-immunoprecipitate under isosmotic conditions and this interaction was disrupted by osmotic stress. Further in silico experiments were conducted to investigate if SIRT1 directly targets NFAT5. In this regard, certain lysine residues of NFAT5, when kept deacetylated, were found to contribute to its DNA binding and SIRT1 was shown to directly bind K282 of NFAT5. Based on these in vitro and in silico findings, SIRT1 was identified, for the first time, as a novel positive regulator of NFAT5 dependent AR expression under osmotic stress in U937 monocytes.
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Affiliation(s)
- Ahmet Can Timucin
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Orhanli, Tuzla, Istanbul, Turkey.
| | - Cagri Bodur
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Orhanli, Tuzla, Istanbul, Turkey.
| | - Huveyda Basaga
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Orhanli, Tuzla, Istanbul, Turkey.
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46
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Shi Y, Guo Y, Hu Y, Li M. Position-specific prediction of methylation sites from sequence conservation based on information theory. Sci Rep 2015. [PMID: 26202727 PMCID: PMC5378888 DOI: 10.1038/srep12403] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.
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Affiliation(s)
- Yinan Shi
- College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Yayun Hu
- College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
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47
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Tissue-specific sequence and structural environments of lysine acetylation sites. J Struct Biol 2015; 191:39-48. [DOI: 10.1016/j.jsb.2015.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 05/29/2015] [Accepted: 06/01/2015] [Indexed: 11/22/2022]
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48
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Pan Y, Karagiannis K, Zhang H, Dingerdissen H, Shamsaddini A, Wan Q, Simonyan V, Mazumder R. Human germline and pan-cancer variomes and their distinct functional profiles. Nucleic Acids Res 2014; 42:11570-88. [PMID: 25232094 PMCID: PMC4191387 DOI: 10.1093/nar/gku772] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.
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Affiliation(s)
- Yang Pan
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Konstantinos Karagiannis
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Haichen Zhang
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Hayley Dingerdissen
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Amirhossein Shamsaddini
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Quan Wan
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA
| | - Vahan Simonyan
- Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA
| | - Raja Mazumder
- The Department of Biochemistry & Molecular Medicine, George Washington University Medical Center, Washington, DC 20037, USA McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
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Zhang N, Zhou Y, Huang T, Zhang YC, Li BQ, Chen L, Cai YD. Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis. PLoS One 2014; 9:e107464. [PMID: 25222670 PMCID: PMC4164654 DOI: 10.1371/journal.pone.0107464] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Accepted: 08/10/2014] [Indexed: 11/18/2022] Open
Abstract
Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role. As experimental approaches are often laborious and time consuming, it is challenging to distinguish the two types of PTMs on lysine residues using computational methods. In this study, we developed a method to discriminate between sumoylated lysine residues and acetylated residues. The method incorporated several features: PSSM conservation scores, amino acid factors, secondary structures, solvent accessibilities and disorder scores. By using the mRMR (Maximum Relevance Minimum Redundancy) method and the IFS (Incremental Feature Selection) method, an optimal feature set was selected from all of the incorporated features, with which the classifier achieved 92.14% accuracy with an MCC value of 0.7322. Analysis of the optimal feature set revealed some differences between acetylation and sumoylation. The results from our study also supported the previous finding that there exist different consensus motifs for the two types of PTMs. The results could suggest possible dominant factors governing the acetylation and sumoylation of lysine residues, shedding some light on the modification dynamics and molecular mechanisms of the two types of PTMs, and provide guidelines for experimental validations.
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Affiliation(s)
- Ning Zhang
- Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, P.R. China
| | - You Zhou
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. China
| | - Tao Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yu-Chao Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. China
| | - Bi-Qing Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, P.R. China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, P.R. China
- * E-mail:
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50
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Evidence supporting the existence of a NUPR1-like family of helix-loop-helix chromatin proteins related to, yet distinct from, AT hook-containing HMG proteins. J Mol Model 2014; 20:2357. [PMID: 25056123 PMCID: PMC4139591 DOI: 10.1007/s00894-014-2357-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 06/15/2014] [Indexed: 12/29/2022]
Abstract
NUPR1, a small chromatin protein, plays a critical role in cancer development, progression, and resistance to therapy. Here, using a combination of structural bioinformatics and molecular modeling methods, we report several novel findings that enhance our understanding of the biochemical function of this protein. We find that NUPR1 has been conserved throughout evolution, and over time it has undergone duplications and transpositions to form other transcriptional regulators. Using threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations, we generated structural models for four of these proteins: NUPR1a, NUPR1b, NUPR2, and the NUPR-like domain of GTF2-I. Comparative analyses of these models combined with extensive linear motif identification reveal that these four proteins, though similar in their propensities for folding, differ in size, surface changes, and sites amenable for posttranslational modification. Lastly, taking NUPR1a as the paradigm for this family, we built models of a NUPR–DNA complex. Additional structural comparisons revealed that NUPR1 defines a new family of small-groove-binding proteins that share structural features with, yet are distinct from, helix-loop-helix AT-hook-containing HMG proteins. These models and inferences should lead to a better understanding of the function of this group of chromatin proteins, which play a critical role in the development of human malignant diseases.
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