1
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Li B, Chen H, Huang J, He B. CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors. Interdiscip Sci 2023; 15:578-589. [PMID: 37389722 DOI: 10.1007/s12539-023-00575-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 07/01/2023]
Abstract
CD47/SIRPα pathway is a new breakthrough in the field of tumor immunity after PD-1/PD-L1. While current monoclonal antibody therapies targeting CD47/SIRPα have demonstrated some anti-tumor effectiveness, there are several inherent limitations associated with these formulations. In the paper, we developed a predictive model that combines next-generation phage display (NGPD) and traditional machine learning methods to distinguish CD47 binding peptides. First, we utilized NGPD biopanning technology to screen CD47 binding peptides. Second, ten traditional machine learning methods based on multiple peptide descriptors and three deep learning methods were used to build computational models for identifying CD47 binding peptides. Finally, we proposed an integrated model based on support vector machine. During the five-fold cross-validation, the integrated predictor demonstrated specificity, accuracy, and sensitivity of 0.755, 0.764, and 0.772, respectively. Furthermore, an online bioinformatics tool called CD47Binder has been developed for the integrated predictor. This tool is readily accessible on http://i.uestc.edu.cn/CD47Binder/cgi-bin/CD47Binder.pl .
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Affiliation(s)
- Bowen Li
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China
| | - Heng Chen
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 6173001, Sichuan, China.
| | - Bifang He
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
- State Key Laboratory of Public Big Data, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
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2
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Zhang H, Saravanan KM, Wei Y, Jiao Y, Yang Y, Pan Y, Wu X, Zhang JZH. Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening. J Chem Inf Model 2023; 63:835-845. [PMID: 36724090 DOI: 10.1021/acs.jcim.2c01485] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Yang Jiao
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518112, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China.,Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xuli Wu
- School of Medicine, Shenzhen University, Shenzhen 518060, Guangdong, China
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China.,East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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3
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Comparative Evaluation of Reproducibility of Phage-Displayed Peptide Selections and NGS Data, through High-Fidelity Mapping of Massive Peptide Repertoires. Int J Mol Sci 2023; 24:ijms24021594. [PMID: 36675109 PMCID: PMC9862337 DOI: 10.3390/ijms24021594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/08/2023] [Accepted: 01/08/2023] [Indexed: 01/14/2023] Open
Abstract
Phage-displayed peptide selections generate complex repertoires of several hundred thousand peptides as revealed by next-generation sequencing (NGS). In repeated peptide selections, however, even in identical experimental in vitro conditions, only a very small number of common peptides are found. The repertoire complexities are evidence of the difficulty of distinguishing between effective selections of specific peptide binders to exposed targets and the potential high background noise. Such investigation is even more relevant when considering the plethora of in vivo expressed targets on cells, in organs or in the entire organism to define targeting peptide agents. In the present study, we compare the published NGS data of three peptide repertoires that were obtained by phage display under identical experimental in vitro conditions. By applying the recently developed tool PepSimili we evaluate the calculated similarities of the individual peptides from each of these three repertoires and perform their mappings on the human proteome. The peptide-to-peptide mappings reveal high similarities among the three repertoires, confirming the desired reproducibility of phage-displayed peptide selections.
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4
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He B, Li B, Chen X, Zhang Q, Lu C, Yang S, Long J, Ning L, Chen H, Huang J. PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors. Front Microbiol 2022; 13:928774. [PMID: 35910615 PMCID: PMC9335124 DOI: 10.3389/fmicb.2022.928774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Monoclonal antibody drugs targeting the PD-1/PD-L1 pathway have showed efficacy in the treatment of cancer patients, however, they have many intrinsic limitations and inevitable drawbacks. Peptide inhibitors as alternatives might compensate for the drawbacks of current PD-1/PD-L1 interaction blockers. Identifying PD-L1 binding peptides by random peptide library screening is a time-consuming and labor-intensive process. Machine learning-based computational models enable rapid discovery of peptide candidates targeting the PD-1/PD-L1 pathway. In this study, we first employed next-generation phage display (NGPD) biopanning to isolate PD-L1 binding peptides. Different peptide descriptors and feature selection methods as well as diverse machine learning methods were then incorporated to implement predictive models of PD-L1 binding. Finally, we proposed PDL1Binder, an ensemble computational model for efficiently obtaining PD-L1 binding peptides. Our results suggest that predictive models of PD-L1 binding can be learned from deep sequencing data and provide a new path to discover PD-L1 binding peptides. A web server was implemented for PDL1Binder, which is freely available at http://i.uestc.edu.cn/pdl1binder/cgi-bin/PDL1Binder.pl.
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Affiliation(s)
- Bifang He
- Medical College, Guizhou University, Guiyang, China
| | - Bowen Li
- Medical College, Guizhou University, Guiyang, China
| | - Xue Chen
- Medical College, Guizhou University, Guiyang, China
| | | | - Chunying Lu
- Medical College, Guizhou University, Guiyang, China
| | | | - Jinjin Long
- Medical College, Guizhou University, Guiyang, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Heng Chen
- Medical College, Guizhou University, Guiyang, China
- *Correspondence: Heng Chen,
| | - Jian Huang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Jian Huang,
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5
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Zambrano N, Froechlich G, Lazarevic D, Passariello M, Nicosia A, De Lorenzo C, Morelli MJ, Sasso E. High-Throughput Monoclonal Antibody Discovery from Phage Libraries: Challenging the Current Preclinical Pipeline to Keep the Pace with the Increasing mAb Demand. Cancers (Basel) 2022; 14:cancers14051325. [PMID: 35267633 PMCID: PMC8909429 DOI: 10.3390/cancers14051325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Monoclonal antibodies are increasingly used for a broad range of diseases. Rising demand must face with time time-consuming and laborious processes to isolate novel monoclonal antibodies. Next-generation sequencing coupled to phage display provides timely and sustainable high throughput selection strategy to rapidly access novel target. Here, we describe the current NGS-guided strategies to identify potential binders from enriched sub-libraires by applying a user-friendly informatic pipeline to identify and discard false positive clones. Rescue step and strategies to boost mAb yield are also discussed to improve the limiting selection and screening steps. Abstract Monoclonal antibodies are among the most powerful therapeutics in modern medicine. Since the approval of the first therapeutic antibody in 1986, monoclonal antibodies keep holding great expectations for application in a range of clinical indications, highlighting the need to provide timely and sustainable access to powerful screening options. However, their application in the past has been limited by time-consuming and expensive steps of discovery and production. The screening of antibody repertoires is a laborious step; however, the implementation of next-generation sequencing-guided screening of single-chain antibody fragments has now largely overcome this issue. This review provides a detailed overview of the current strategies for the identification of monoclonal antibodies from phage display-based libraries. We also discuss the challenges and the possible solutions to improve the limiting selection and screening steps, in order to keep pace with the increasing demand for monoclonal antibodies.
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Affiliation(s)
- Nicola Zambrano
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
- Correspondence: (N.Z.); (E.S.)
| | - Guendalina Froechlich
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Dejan Lazarevic
- Center for Omics Sciences Ospedale San Raffaele, Via Olgettina 58, 20132 Milano, Italy; (D.L.); (M.J.M.)
| | - Margherita Passariello
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Alfredo Nicosia
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Claudia De Lorenzo
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Marco J. Morelli
- Center for Omics Sciences Ospedale San Raffaele, Via Olgettina 58, 20132 Milano, Italy; (D.L.); (M.J.M.)
| | - Emanuele Sasso
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
- Correspondence: (N.Z.); (E.S.)
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6
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He B, Yang S, Long J, Chen X, Zhang Q, Gao H, Chen H, Huang J. TUPDB: Target-Unrelated Peptide Data Bank. Interdiscip Sci 2021; 13:426-432. [PMID: 33993461 DOI: 10.1007/s12539-021-00436-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/29/2022]
Abstract
The isolation of target-unrelated peptides (TUPs) through biopanning remains as a major problem of phage display selection experiments. These TUPs do not have any actual affinity toward targets of interest, which tend to be mistakenly identified as target-binding peptides. Therefore, an information portal for storing TUP data is urgently needed. Here, we present a TUP data bank (TUPDB), which is a comprehensive, manually curated database of approximately 73 experimentally verified TUPs and 1963 potential TUPs collected from TUPScan, the BDB database, and public research articles. The TUPScan tool has been integrated in TUPDB to facilitate TUP analysis. We believe that TUPDB can help identify and remove TUPs in future reports in the biopanning community. The database is of great importance to improving the quality of phage display-based epitope mapping and promoting the development of vaccines, diagnostics, and therapeutics. The TUPDB database is available at http://i.uestc.edu.cn/tupdb .
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Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang, 550025, China. .,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Shanshan Yang
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Jinjin Long
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Xue Chen
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Qianyue Zhang
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Hui Gao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang, 550025, China.
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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7
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Puccio S, Grillo G, Consiglio A, Soluri MF, Sblattero D, Cotella D, Santoro C, Liuni S, Bellis GD, Lugli E, Peano C, Licciulli F. InteractomeSeq: a web server for the identification and profiling of domains and epitopes from phage display and next generation sequencing data. Nucleic Acids Res 2020; 48:W200-W207. [PMID: 32402076 PMCID: PMC7319578 DOI: 10.1093/nar/gkaa363] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 04/16/2020] [Accepted: 05/05/2020] [Indexed: 01/03/2023] Open
Abstract
High-Throughput Sequencing technologies are transforming many research fields, including the analysis of phage display libraries. The phage display technology coupled with deep sequencing was introduced more than a decade ago and holds the potential to circumvent the traditional laborious picking and testing of individual phage rescued clones. However, from a bioinformatics point of view, the analysis of this kind of data was always performed by adapting tools designed for other purposes, thus not considering the noise background typical of the 'interactome sequencing' approach and the heterogeneity of the data. InteractomeSeq is a web server allowing data analysis of protein domains ('domainome') or epitopes ('epitome') from either Eukaryotic or Prokaryotic genomic phage libraries generated and selected by following an Interactome sequencing approach. InteractomeSeq allows users to upload raw sequencing data and to obtain an accurate characterization of domainome/epitome profiles after setting the parameters required to tune the analysis. The release of this tool is relevant for the scientific and clinical community, because InteractomeSeq will fill an existing gap in the field of large-scale biomarkers profiling, reverse vaccinology, and structural/functional studies, thus contributing essential information for gene annotation or antigen identification. InteractomeSeq is freely available at https://InteractomeSeq.ba.itb.cnr.it/.
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Affiliation(s)
- Simone Puccio
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center, IRCCS, Rozzano (Milan), 20089, Italy
| | - Giorgio Grillo
- Institute for Biomedical Technologies, National Research Council, Bari 70100, Italy
| | - Arianna Consiglio
- Institute for Biomedical Technologies, National Research Council, Bari 70100, Italy
| | - Maria Felicia Soluri
- Department of Health Sciences & Center for TranslationalResearch on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara 28100, Italy
| | - Daniele Sblattero
- Department of Life Sciences, University of Trieste, Trieste 34100, Italy
| | - Diego Cotella
- Department of Health Sciences & Center for TranslationalResearch on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara 28100, Italy
| | - Claudio Santoro
- Department of Health Sciences & Center for TranslationalResearch on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara 28100, Italy
| | - Sabino Liuni
- Institute for Biomedical Technologies, National Research Council, Bari 70100, Italy
| | - Gianluca De Bellis
- Institute for Biomedical Technologies, National Research Council, Segrate (Milan) 20090, Italy
| | - Enrico Lugli
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center, IRCCS, Rozzano (Milan), 20089, Italy.,Humanitas Flow Cytometry Core, Humanitas Clinical and Research Center, IRCCS, Rozzano (Milan) 20089, Italy
| | - Clelia Peano
- Institute of Genetic and Biomedical Research, UoS Milan, National Research Council, Rozzano (Milan) 20089, Italy.,Genomic Unit, Humanitas Clinical and Research Center, IRCCS,Rozzano (Milan) 20089, Italy
| | - Flavio Licciulli
- Institute for Biomedical Technologies, National Research Council, Bari 70100, Italy
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8
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Wang Y, Kang J, Li N, Zhou Y, Tang Z, He B, Huang J. NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors. Protein Pept Lett 2020; 27:337-345. [PMID: 31721688 DOI: 10.2174/0929866526666191112150636] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 07/16/2019] [Accepted: 09/24/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. OBJECTIVE With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors' cleavage sites quickly and efficiently. METHODS We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. RESULTS Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. CONCLUSION The performance of the developed model was decent. Moreover, for users' convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors' cleavage sites effectively.
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Affiliation(s)
- Ying Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Juanjuan Kang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ning Li
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuwei Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhongjie Tang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Medical College, Guizhou University, Guiyang, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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9
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Huang Z, Gu RX. Development and Application of Computational Methods in Biology and Medicine. Curr Med Chem 2020; 26:7534-7536. [PMID: 31942848 DOI: 10.2174/092986732642200108101400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Zunnan Huang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics Dongguan Scientific Research Center Guangdong Medical University Dongguan, Guangdong 523808, China
| | - Ruo-Xu Gu
- Department of Biological Sciences University of Calgary 2500 University Dr., N. W., Calgary T2N1N4, Canada
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10
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He B, Chen H, Li N, Huang J. SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data. Int J Biol Sci 2019; 15:1452-1459. [PMID: 31337975 PMCID: PMC6643146 DOI: 10.7150/ijbs.31957] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 04/09/2019] [Indexed: 01/13/2023] Open
Abstract
SAROTUP (Scanner And Reporter Of Target-Unrelated Peptides) 3.1 is a significant upgrade to the widely used SAROTUP web server for the rapid identification of target-unrelated peptides (TUPs) in phage display data. At present, SAROTUP has gathered a suite of tools for finding potential TUPs and other purposes. Besides the TUPScan, the motif-based tool, and three tools based on the BDB database, i.e., MimoScan, MimoSearch, and MimoBlast, three predictors based on support vector machine, i.e., PhD7Faster, SABinder and PSBinder, are integrated into SAROTUP. The current version of SAROTUP contains 27 TUP motifs and 823 TUP sequences. We also developed the standalone SAROTUP application with graphical user interface (GUI) and command line versions for processing deep sequencing phage display data and distributed it as an open source package, which can perform perfectly locally on almost all systems that support C++ with little or no modification. The web interfaces of SAROTUP have also been redesigned to be more self-evident and user-friendly. The latest version of SAROTUP is freely available at http://i.uestc.edu.cn/sarotup3.
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Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang 550025, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang 550025, China
| | - Ning Li
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
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11
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Abbasgholizadeh R, Zhang H, Craft JW, Bryan RM, Bark SJ, Briggs JM, Fox RO, Agarkov A, Zimmer WE, Gilbertson SR, Schwartz RJ. Discovery of vascular Rho kinase (ROCK) inhibitory peptides. Exp Biol Med (Maywood) 2019; 244:940-951. [PMID: 31132884 DOI: 10.1177/1535370219849581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Reza Abbasgholizadeh
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA.,2 Texas Medical Center, Texas Heart Institute, Houston, TX 77024, USA
| | - Hua Zhang
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA
| | - John W Craft
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA.,2 Texas Medical Center, Texas Heart Institute, Houston, TX 77024, USA
| | - Robert M Bryan
- 3 Department of Anesthesiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Steven J Bark
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA
| | - James M Briggs
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA
| | - Robert O Fox
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA
| | - Anton Agarkov
- 4 Department of Chemistry, University of Houston, Houston, TX 77024, USA
| | - Warren E Zimmer
- 5 Department of Medical Physiology, Texas A&M Health Science Center, College Station, TX 77843, USA
| | - Scott R Gilbertson
- 4 Department of Chemistry, University of Houston, Houston, TX 77024, USA
| | - Robert J Schwartz
- 1 Department of Biology and Biochemistry, University of Houston, Houston, TX 77024, USA.,2 Texas Medical Center, Texas Heart Institute, Houston, TX 77024, USA
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