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Liu Y, Wang S, Li X, Liu Y, Zhu X. NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT. J Proteome Res 2023; 22:718-728. [PMID: 36749151 DOI: 10.1021/acs.jproteome.2c00363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases. Identification of neuropeptides is of great benefit for studying the mechanism of these physiological processes and the treatment of neurological disorders. Several state-of-the-art neuropeptide predictors have been developed by using a two-layer stacking ensemble algorithm. Although the two-layer stacking ensemble algorithm can improve the feature representability, these models are complex, which are not as efficient as the models based on one classifier. In this study, we proposed a new model, NeuroPpred-SVM, to predict neuropeptides based on the embeddings of Bidirectional Encoder Representations from Transformers and other sequential features by using a support vector machine (SVM). The experimental results indicate that our model achieved a cross-validation area under the receiver operating characteristic (AUROC) curve of 0.969 on the training data set and an AUROC of 0.966 on the independent test set. By comparing our model with the other four state-of-the-art models including NeuroPIpred, PredNeuroP, NeuroPpred-Fuse, and NeuroPpred-FRL on the independent test set, our model achieved the highest AUROC, Matthews correlation coefficient, accuracy, and specificity, which indicate that our model outperforms the existing models. We believed that NeuroPpred-SVM could be a useful tool for identifying neuropeptides with high accuracy and low cost. The data sets and Python code are available at https://github.com/liuyf-a/NeuroPpred-SVM.
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Affiliation(s)
- Yufeng Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Shuyu Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiang Li
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Yinbo Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
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Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning. Brief Bioinform 2021; 22:6272801. [PMID: 33975333 DOI: 10.1093/bib/bbab167] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/23/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.
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Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.,Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | | | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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Mendel HC, Kaas Q, Muttenthaler M. Neuropeptide signalling systems - An underexplored target for venom drug discovery. Biochem Pharmacol 2020; 181:114129. [PMID: 32619425 PMCID: PMC7116218 DOI: 10.1016/j.bcp.2020.114129] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 01/06/2023]
Abstract
Neuropeptides are signalling molecules mainly secreted from neurons that act as neurotransmitters or peptide hormones to affect physiological processes and modulate behaviours. In humans, neuropeptides are implicated in numerous diseases and understanding their role in physiological processes and pathologies is important for therapeutic development. Teasing apart the (patho)physiology of neuropeptides remains difficult due to ligand and receptor promiscuity and the complexity of the signalling pathways. The current approach relies on a pharmacological toolbox of agonists and antagonists displaying high selectivity for independent receptor subtypes, with the caveat that only few selective ligands have been discovered or developed. Animal venoms represent an underexplored source for novel receptor subtype-selective ligands that could aid in dissecting human neuropeptide signalling systems. Multiple endogenous-like neuropeptides as well as peptides acting on neuropeptide receptors are present in venoms. In this review, we summarise current knowledge on neuropeptides and discuss venoms as a source for ligands targeting neuropeptide signalling systems.
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Affiliation(s)
- Helen C Mendel
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Quentin Kaas
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Markus Muttenthaler
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; University of Vienna, Faculty of Chemistry, Institute of Biological Chemistry, Vienna, Austria.
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Brandes N, Ofer D, Linial M. ASAP: a machine learning framework for local protein properties. Database (Oxford) 2016; 2016:baw133. [PMID: 27694209 PMCID: PMC5045867 DOI: 10.1093/database/baw133] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 08/08/2016] [Accepted: 08/28/2016] [Indexed: 11/14/2022]
Abstract
Determining residue-level protein properties, such as sites of post-translational modifications (PTMs), is vital to understanding protein function. Experimental methods are costly and time-consuming, while traditional rule-based computational methods fail to annotate sites lacking substantial similarity. Machine Learning (ML) methods are becoming fundamental in annotating unknown proteins and their heterogeneous properties. We present ASAP (Amino-acid Sequence Annotation Prediction), a universal ML framework for predicting residue-level properties. ASAP extracts numerous features from raw sequences, and supports easy integration of external features such as secondary structure, solvent accessibility, intrinsically disorder or PSSM profiles. Features are then used to train ML classifiers. ASAP can create new classifiers within minutes for a variety of tasks, including PTM prediction (e.g. cleavage sites by convertase, phosphoserine modification). We present a detailed case study for ASAP: CleavePred, an ASAP-based model to predict protein precursor cleavage sites, with state-of-the-art results. Protein cleavage is a PTM shared by a wide variety of proteins sharing minimal sequence similarity. Current rule-based methods suffer from high false positive rates, making them suboptimal. The high performance of CleavePred makes it suitable for analyzing new proteomes at a genomic scale. The tool is attractive to protein design, mass spectrometry search engines and the discovery of new bioactive peptides from precursors. ASAP functions as a baseline approach for residue-level protein sequence prediction. CleavePred is freely accessible as a web-based application. Both ASAP and CleavePred are open-source with a flexible Python API.Database URL: ASAP's and CleavePred source code, webtool and tutorials are available at: https://github.com/ddofer/asap; http://protonet.cs.huji.ac.il/cleavepred.
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Affiliation(s)
- Nadav Brandes
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel
| | - Dan Ofer
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel
| | - Michal Linial
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel
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Li L, Luo Q, Xiao W, Li J, Zhou S, Li Y, Zheng X, Yang H. A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features. J Bioinform Comput Biol 2016; 15:1650025. [PMID: 27411307 DOI: 10.1142/s0219720016500256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.
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Affiliation(s)
- Liqi Li
- * Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Qifa Luo
- * Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Weidong Xiao
- * Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Jinhui Li
- * Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Shiwen Zhou
- † National Drug Clinical Trial Institution, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Yongsheng Li
- ‡ Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
| | - Xiaoqi Zheng
- § Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
| | - Hua Yang
- * Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
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Ofer D, Linial M. ProFET: Feature engineering captures high-level protein functions. Bioinformatics 2015; 31:3429-36. [DOI: 10.1093/bioinformatics/btv345] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 05/29/2015] [Indexed: 11/13/2022] Open
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The Little Known Universe of Short Proteins in Insects: A Machine Learning Approach. SHORT VIEWS ON INSECT GENOMICS AND PROTEOMICS 2015. [DOI: 10.1007/978-3-319-24235-4_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Li L, Yu S, Xiao W, Li Y, Huang L, Zheng X, Zhou S, Yang H. Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM. BMC Bioinformatics 2014; 15:340. [PMID: 25409550 PMCID: PMC4289199 DOI: 10.1186/1471-2105-15-340] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 09/29/2014] [Indexed: 02/08/2023] Open
Abstract
Background Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed. Results Here we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools. Conclusions Comparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.
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Affiliation(s)
| | | | | | | | | | - Xiaoqi Zheng
- Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China.
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