1
|
Meng Y, Zhang L, Zhang L, Wang Z, Wang X, Li C, Chen Y, Shang S, Li L. CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications. Brief Bioinform 2022; 23:6775608. [PMID: 36305460 PMCID: PMC9677505 DOI: 10.1093/bib/bbac460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/27/2022] [Accepted: 09/26/2022] [Indexed: 12/14/2022] Open
Abstract
The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.
Collapse
Affiliation(s)
| | | | - Laizhi Zhang
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Ziyu Wang
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Xuanwen Wang
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Chan Li
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Yu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Shipeng Shang
- Corresponding authors: Lei Li, Faculty of Biomedical and Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao 266071, China. Tel/Fax: +86 532 8581 2983; E-mail: ; Shipeng Shang, School of Basic Medicine, Qingdao University, Qingdao 266071, China. Tel.: +86 532 8595 1111; Fax: +86 532 8581 2983; E-mail:
| | - Lei Li
- Corresponding authors: Lei Li, Faculty of Biomedical and Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao 266071, China. Tel/Fax: +86 532 8581 2983; E-mail: ; Shipeng Shang, School of Basic Medicine, Qingdao University, Qingdao 266071, China. Tel.: +86 532 8595 1111; Fax: +86 532 8581 2983; E-mail:
| |
Collapse
|
2
|
FRTpred: A novel approach for accurate prediction of protein folding rate and type. Comput Biol Med 2022; 149:105911. [DOI: 10.1016/j.compbiomed.2022.105911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/08/2022] [Accepted: 07/23/2022] [Indexed: 11/20/2022]
|
3
|
Li F, Dong S, Leier A, Han M, Guo X, Xu J, Wang X, Pan S, Jia C, Zhang Y, Webb GI, Coin LJM, Li C, Song J. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Brief Bioinform 2021; 23:6415313. [PMID: 34729589 DOI: 10.1093/bib/bbab461] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 12/14/2022] Open
Abstract
Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.
Collapse
Affiliation(s)
- Fuyi Li
- Monash University, Australia
| | | | - André Leier
- Department of Genetics, UAB School of Medicine, USA
| | - Meiya Han
- Department of Biochemistry and Molecular Biology, Monash University, Australia
| | | | - Jing Xu
- Computer Science and Technology from Nankai University, China
| | - Xiaoyu Wang
- Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia
| | - Shirui Pan
- University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Cangzhi Jia
- College of Science, Dalian Maritime University, Australia
| | - Yang Zhang
- Northwestern Polytechnical University, China
| | - Geoffrey I Webb
- Faculty of Information Technology at Monash University, Australia
| | - Lachlan J M Coin
- Department of Clinical Pathology, University of Melbourne, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry of Molecular Biology, Monash University, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Melbourne, Australia
| |
Collapse
|
4
|
Yan K, Wen J, Xu Y, Liu B. Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2682-2691. [PMID: 32356759 DOI: 10.1109/tcbb.2020.2991268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods that combine the various features to improve predictive performance remain the challenge problems. In this study, we proposed two novel algorithms: AWMG and EMfold. AWMG used a novel predictor based on the multi-view learning framework for fold recognition. Each view was treated as the intermediate representation of the corresponding data source of proteins, including the evolutionary information and the retrieval information. AWMG calculated the auto-weight for each view respectively and constructed the latent subspace which contains the common information shared by different views. The marginalized constraint was employed to enlarge the margins between different folds, improving the predictive performance of AWMG. Furthermore, we proposed a novel ensemble method called EMfold, which combines two complementary methods AWMG and DeepSS. The later method was a template-based algorithm using the SPARKS-X and DeepFR programs. EMfold integrated the advantages of template-based assignment and machine learning classifier. Experimental results on the two widely datasets (LE and YK) showed that the proposed methods outperformed some state-of-the-art methods, indicating that AWMG and EMfold are useful tools for protein fold recognition.
Collapse
|
5
|
Nallapareddy V, Bogam S, Devarakonda H, Paliwal S, Bandyopadhyay D. DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains. Proteins 2021; 89:745-761. [PMID: 33580578 DOI: 10.1002/prot.26056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/31/2021] [Indexed: 12/29/2022]
Abstract
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scarce, in contrast to specific function prediction algorithms. Here we present a deep neural network-based multiple Cys function prediction, available on web-server (DeepCys) (https://deepcys.herokuapp.com/). DeepCys model was trained and tested on two independent datasets curated from protein crystal structures. This prediction method requires three inputs, namely, PDB identifier (ID), chain ID and residue ID for a given Cys and outputs the probabilities of four cysteine functions, namely, disulphide, metal-binding, thioether and sulphenylation and predicts the most probable Cys function. The algorithm exploits the local and global protein properties, like, sequence and secondary structure motifs, buried fractions, microenvironments and protein/enzyme class. DeepCys outperformed most of the multiple and specific Cys function algorithms. This method can predict maximum number of cysteine functions. Moreover, for the first time, explicitly predicts thioether function. This tool was used to elucidate the cysteine functions on domains of unknown functions belonging to cytochrome C oxidase subunit-II like transmembrane domains. Apart from the web-server, a standalone program is also available on GitHub (https://github.com/vam-sin/deepcys).
Collapse
Affiliation(s)
- Vamsi Nallapareddy
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Bogam
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Himaja Devarakonda
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Paliwal
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Debashree Bandyopadhyay
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| |
Collapse
|
6
|
Manavalan B, Basith S, Shin TH, Lee G. Computational prediction of species-specific yeast DNA replication origin via iterative feature representation. Brief Bioinform 2020; 22:6000361. [PMID: 33232970 PMCID: PMC8294535 DOI: 10.1093/bib/bbaa304] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 12/13/2022] Open
Abstract
Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.
Collapse
Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Tae Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| |
Collapse
|
7
|
Li F, Chen J, Ge Z, Wen Y, Yue Y, Hayashida M, Baggag A, Bensmail H, Song J. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework. Brief Bioinform 2020; 22:2126-2140. [PMID: 32363397 DOI: 10.1093/bib/bbaa049] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/25/2020] [Accepted: 03/11/2020] [Indexed: 12/12/2022] Open
Abstract
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.
Collapse
Affiliation(s)
- Fuyi Li
- Northwest A&F University, China.,Department of Biochemistry and Molecular Biology and the Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Australia
| | - Jinxiang Chen
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University from the College of Information Engineering, Northwest A&F University, China
| | - Zongyuan Ge
- Monash University and also serves as a Deep Learning Specialist at NVIDIA AI Technology Centre. Before joining Monash, he was a research scientist at IBM Research Australia doing research in medical AI during 2016-2018. His research interests are AI, computer vision, medical image, robotics and deep learning
| | - Ya Wen
- computer technology from Ningxia University, China
| | - Yanwei Yue
- medical science from Southern Medical University, China
| | - Morihiro Hayashida
- informatics from Kyoto University, Japan, in 2005. He is an Assistant Professor in the Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Japan
| | - Abdelkader Baggag
- computer science from the University of Minnesota. He is a Senior Scientist at the Qatar Computing Research Institute (QCRI) and has a joint appointment as an Associate Professor at Hamad Bin Khalifa University (HBKU) in the Division of Information and Computing Technology. His research interests include data mining, linear algebra and machine learning
| | - Halima Bensmail
- University of Pierre & Marie Currie (Paris 6) in France. She is currently a Principal Scientist at QCRI-HBKU and a joint Associate Professor at the College of Computer and Science Engineering, HBKU
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Australia. He is also affiliated with the Monash Centre for Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining, and pattern recognition
| |
Collapse
|
8
|
Zhu Y, Jia C, Li F, Song J. Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Anal Biochem 2020; 593:113592. [DOI: 10.1016/j.ab.2020.113592] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/14/2020] [Accepted: 01/17/2020] [Indexed: 12/13/2022]
|