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Zhang Y, Liu L, Zhang M, Li S, Wu J, Sun Q, Ma S, Cai W. The Research Progress of Bioactive Peptides Derived from Traditional Natural Products in China. Molecules 2023; 28:6421. [PMID: 37687249 PMCID: PMC10489889 DOI: 10.3390/molecules28176421] [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: 07/29/2023] [Revised: 08/20/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Traditional natural products in China have a long history and a vast pharmacological repertoire that has garnered significant attention due to their safety and efficacy in disease prevention and treatment. Among the bioactive components of traditional natural products in China, bioactive peptides (BPs) are specific protein fragments that have beneficial effects on human health. Despite many of the traditional natural products in China ingredients being rich in protein, BPs have not received sufficient attention as a critical factor influencing overall therapeutic efficacy. Therefore, the purpose of this review is to provide a comprehensive summary of the current methodologies for the preparation, isolation, and identification of BPs from traditional natural products in China and to classify the functions of discovered BPs. Insights from this review are expected to facilitate the development of targeted drugs and functional foods derived from traditional natural products in China in the future.
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Affiliation(s)
- Yanyan Zhang
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China; (Y.Z.); (Q.S.)
| | - Lianghong Liu
- School of Pharmaceutical Sciences, Hunan University of Medicine, Huaihua 418000, China; (L.L.); (M.Z.); (S.L.); (J.W.)
| | - Min Zhang
- School of Pharmaceutical Sciences, Hunan University of Medicine, Huaihua 418000, China; (L.L.); (M.Z.); (S.L.); (J.W.)
| | - Shani Li
- School of Pharmaceutical Sciences, Hunan University of Medicine, Huaihua 418000, China; (L.L.); (M.Z.); (S.L.); (J.W.)
| | - Jini Wu
- School of Pharmaceutical Sciences, Hunan University of Medicine, Huaihua 418000, China; (L.L.); (M.Z.); (S.L.); (J.W.)
| | - Qiuju Sun
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China; (Y.Z.); (Q.S.)
| | - Shengjun Ma
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China; (Y.Z.); (Q.S.)
| | - Wei Cai
- School of Pharmaceutical Sciences, Hunan University of Medicine, Huaihua 418000, China; (L.L.); (M.Z.); (S.L.); (J.W.)
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Ng CCA, Zhou Y, Yao ZP. Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review. Anal Chim Acta 2023; 1268:341330. [PMID: 37268337 DOI: 10.1016/j.aca.2023.341330] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 06/04/2023]
Abstract
Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.
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Affiliation(s)
- Cheuk Chi A Ng
- State Key Laboratory of Chemical Biology and Drug Discovery, and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; Research Institute for Future Food, and Research Center for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation), and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, China
| | - Yin Zhou
- State Key Laboratory of Chemical Biology and Drug Discovery, and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; Research Institute for Future Food, and Research Center for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation), and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, China
| | - Zhong-Ping Yao
- State Key Laboratory of Chemical Biology and Drug Discovery, and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; Research Institute for Future Food, and Research Center for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China; State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation), and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, China.
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Ng CCA, Tam WM, Yin H, Wu Q, So PK, Wong MYM, Lau FCM, Yao ZP. Data storage using peptide sequences. Nat Commun 2021; 12:4242. [PMID: 34257289 PMCID: PMC8277807 DOI: 10.1038/s41467-021-24496-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Humankind is generating digital data at an exponential rate. These data are typically stored using electronic, magnetic or optical devices, which require large physical spaces and cannot last for a very long time. Here we report the use of peptide sequences for data storage, which can be durable and of high storage density. With the selection of suitable constitutive amino acids, designs of address codes and error-correction schemes to protect the order and integrity of the stored data, optimization of the analytical protocol and development of a software to effectively recover peptide sequences from the tandem mass spectra, we demonstrated the feasibility of this method by successfully storing and retrieving a text file and the music file Silent Night with 40 and 511 18-mer peptides respectively. This method for the first time links data storage with the peptide synthesis industry and proteomics techniques, and is expected to stimulate the development of relevant fields.
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Affiliation(s)
- Cheuk Chi A Ng
- State Key Laboratory of Chemical Biology and Drug Discovery, Research Institute for Future Food and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China
| | - Wai Man Tam
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Haidi Yin
- State Key Laboratory of Chemical Biology and Drug Discovery, Research Institute for Future Food and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China
| | - Qian Wu
- State Key Laboratory of Chemical Biology and Drug Discovery, Research Institute for Future Food and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China
| | - Pui-Kin So
- University Research Facility in Life Sciences, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Melody Yee-Man Wong
- University Research Facility in Chemical and Environmental Analysis, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Francis C M Lau
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
| | - Zhong-Ping Yao
- State Key Laboratory of Chemical Biology and Drug Discovery, Research Institute for Future Food and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
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Yan Y, Kusalik AJ, Wu FX. NovoExD: De novo Peptide Sequencing for ETD/ECD Spectra. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:337-344. [PMID: 28368811 DOI: 10.1109/tcbb.2015.2389813] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
De novo peptide sequencing using tandem mass spectrometry (MS/MS) data has become a major computational method for sequence identification in recent years. With the development of new instruments and technology, novel computational methods have emerged with enhanced performance. However, there are only a few methods focusing on ECD/ETD spectra, which mainly contain variants of c -ions and z-ions. Here, a de novo sequencing method for ECD/ETD spectra, NovoExD, is presented. NovoExD applies a new form of spectrum graph with multiple edge types (called a GMET), considers multiple peptide tags, and integrates amino acid combination (AAC) and fragment ion charge information. Its performance is compared with another successful de novo sequencing method, pNovo+, which has an option for ECD/ETD spectra. Experiments conducted on three different datasets show that the average full length peptide identification accuracy of NovoExD is as high as 88.70 percent, and that NovoExD's average accuracy is more than 20 percent greater on all datasets than that of pNovo+.
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Abstract
Background De novo peptide sequencing via tandem mass spectrometry (MS/MS) has been developed rapidly in recent years. With the use of spectra pairs from the same peptide under different fragmentation modes, performance of de novo sequencing is greatly improved. Currently, with large amount of spectra sequenced everyday, spectra libraries containing tens of thousands of annotated experimental MS/MS spectra become available. These libraries provide information of the spectra properties, thus have the potential to be used with de novo sequencing to improve its performance. Results In this study, an improved de novo sequencing method assisted with spectra library is proposed. It uses spectra libraries as training datasets and introduces significant scores of the features used in our previous de novo sequencing method for HCD and ETD spectra pairs. Two pairs of HCD and ETD spectral datasets were used to test the performance of the proposed method and our previous method. The results show that this proposed method achieves better sequencing accuracy with higher ranked correct sequences and less computational time. Conclusions This paper proposed an advanced de novo sequencing method for HCD and ETD spectra pair and used information from spectra libraries and significant improved previous similar methods.
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Affiliation(s)
- Yan Yan
- Department of Cumputer Science, Faculty of Science, University of Western Ontario, London, Canada
| | - Kaizhong Zhang
- Department of Cumputer Science, Faculty of Science, University of Western Ontario, London, Canada.
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Yan Y, Kusalik AJ, Wu FX. De novopeptide sequencing using CID and HCD spectra pairs. Proteomics 2016; 16:2615-2624. [DOI: 10.1002/pmic.201500251] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 05/31/2016] [Accepted: 07/08/2016] [Indexed: 11/06/2022]
Affiliation(s)
- Yan Yan
- Division; of Biomedical Engineering; University of Saskatchewan; Saskatoon Saskatchewan Canada
| | - Anthony J. Kusalik
- Division; of Biomedical Engineering; University of Saskatchewan; Saskatoon Saskatchewan Canada
- Department of Computer Science; University of Saskatchewan; Saskatoon Saskatchewan Canada
| | - Fang-Xiang Wu
- Division; of Biomedical Engineering; University of Saskatchewan; Saskatoon Saskatchewan Canada
- Department of Mechanical Engineering; University of Saskatchewan; Saskatoon Saskatchewan Canada
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Gorshkov V, Hotta SYK, Verano-Braga T, Kjeldsen F. Peptide de novo sequencing of mixture tandem mass spectra. Proteomics 2016; 16:2470-9. [PMID: 27329701 PMCID: PMC5297990 DOI: 10.1002/pmic.201500549] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 04/27/2016] [Accepted: 06/17/2016] [Indexed: 02/02/2023]
Abstract
The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co‐isolation and thus prone to false identifications. The deconvolution approach matched complementary b‐, y‐ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co‐isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20–35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.
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Affiliation(s)
- Vladimir Gorshkov
- Department of Biochemistry and Molecular Biology, University of Southern Denmark Odense M, Odense, Denmark.
| | | | - Thiago Verano-Braga
- Department of Biochemistry and Molecular Biology, University of Southern Denmark Odense M, Odense, Denmark.,Department of Physiology and Biophysics, Federal University of Minas Gerais Belo Horizonte - MG, Belo Horizonte, Brazil
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark Odense M, Odense, Denmark
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Sadygov RG. Using SEQUEST with theoretically complete sequence databases. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2015; 26:1858-1864. [PMID: 26238326 PMCID: PMC4607654 DOI: 10.1007/s13361-015-1228-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 05/08/2015] [Accepted: 06/17/2015] [Indexed: 06/04/2023]
Abstract
SEQUEST has long been used to identify peptides/proteins from their tandem mass spectra and protein sequence databases. The algorithm has proven to be hugely successful for its sensitivity and specificity in identifying peptides/proteins, the sequences of which are present in the protein sequence databases. In this work, we report on work that attempts a new use for the algorithm by applying it to search a complete list of theoretically possible peptides, a de novo-like sequencing. We used freely available mass spectral data and determined a number of unique peptides as identified by SEQUEST. Using masses of these peptides and the mass accuracy of 0.001 Da, we have created a database of all theoretically possible peptide sequences corresponding to the precursor masses. We used our recently developed algorithm for determining all amino acid compositions corresponding to a mass interval, and used a lexicographic ordering to generate theoretical sequences from the compositions. The newly generated theoretical database was many-fold more complex than the original protein sequence database. We used SEQUEST to search and identify the best matches to the spectra from all theoretically possible peptide sequences. We found that SEQUEST cross-correlation score ranked the correct peptide match among the top sequence matches. The results testify to the high specificity of SEQUEST when combined with the high mass accuracy for intact peptides. Graphical Abstract ᅟ.
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Affiliation(s)
- Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, 77555, USA.
- Sealy Center for Molecular Medicine, The University of Texas Medical Branch, Galveston, TX, 77555, USA.
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Yan Y, Kusalik AJ, Wu FX. A Framework of De Novo Peptide Sequencing for Multiple Tandem Mass Spectra. IEEE Trans Nanobioscience 2015; 14:478-484. [DOI: 10.1109/tnb.2015.2419194] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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