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Reifenberg P, Zimmer A. Branched-chain amino acids: physico-chemical properties, industrial synthesis and role in signaling, metabolism and energy production. Amino Acids 2024; 56:51. [PMID: 39198298 PMCID: PMC11358235 DOI: 10.1007/s00726-024-03417-2] [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: 06/13/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024]
Abstract
Branched-chain amino acids (BCAAs)-leucine (Leu), isoleucine (Ile), and valine (Val)-are essential nutrients with significant roles in protein synthesis, metabolic regulation, and energy production. This review paper offers a detailed examination of the physico-chemical properties of BCAAs, their industrial synthesis, and their critical functions in various biological processes. The unique isomerism of BCAAs is presented, focusing on analytical challenges in their separation and quantification as well as their solubility characteristics, which are crucial for formulation and purification applications. The industrial synthesis of BCAAs, particularly using bacterial strains like Corynebacterium glutamicum, is explored, alongside methods such as genetic engineering aimed at enhancing production, detailing the enzymatic processes and specific precursors. The dietary uptake, distribution, and catabolism of BCAAs are reviewed as fundamental components of their physiological functions. Ultimately, their multifaceted impact on signaling pathways, immune function, and disease progression is discussed, providing insights into their profound influence on muscle protein synthesis and metabolic health. This comprehensive analysis serves as a resource for understanding both the basic and complex roles of BCAAs in biological systems and their industrial application.
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Affiliation(s)
- Philipp Reifenberg
- Merck Life Science KGaA, Upstream R&D, Frankfurter Strasse 250, 64293, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Alarich‑Weiss‑Strasse 4, 64287, Darmstadt, Germany
| | - Aline Zimmer
- Merck Life Science KGaA, Upstream R&D, Frankfurter Strasse 250, 64293, Darmstadt, Germany.
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Chen Y, Du Z, Zhao H, Fang W, Liu T, Zhang Y, Zhang W, Qin W. SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification. Anal Chim Acta 2023; 1279:341793. [PMID: 37827637 DOI: 10.1016/j.aca.2023.341793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/26/2023] [Accepted: 09/06/2023] [Indexed: 10/14/2023]
Abstract
Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the "entrapment database" strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.
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Affiliation(s)
- Yongle Chen
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Zhuokun Du
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Hongxian Zhao
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Wei Fang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Tong Liu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Yangjun Zhang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China
| | - Wanjun Zhang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China; College of Chemistry and Materials Science, Hebei University, Baoding, 071002, China
| | - Weijie Qin
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, Beijing, 102206, PR China; College of Chemistry and Materials Science, Hebei University, Baoding, 071002, China.
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Beslic D, Tscheuschner G, Renard BY, Weller MG, Muth T. Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. Brief Bioinform 2022; 24:6955273. [PMID: 36545804 PMCID: PMC9851299 DOI: 10.1093/bib/bbac542] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/25/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.
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Affiliation(s)
- Denis Beslic
- Corresponding authors: D. Beslic, Robert Koch Institute, ZKI-PH 3, Nordufer 20, 13353 Berlin, Germany. E-mail: ; G. Tscheuschner, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; B.Y. Renard, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany. E-mail: ; M.G. Weller, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; T. Muth, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany. E-mail:
| | - Georg Tscheuschner
- Corresponding authors: D. Beslic, Robert Koch Institute, ZKI-PH 3, Nordufer 20, 13353 Berlin, Germany. E-mail: ; G. Tscheuschner, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; B.Y. Renard, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany. E-mail: ; M.G. Weller, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; T. Muth, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany. E-mail:
| | - Bernhard Y Renard
- Corresponding authors: D. Beslic, Robert Koch Institute, ZKI-PH 3, Nordufer 20, 13353 Berlin, Germany. E-mail: ; G. Tscheuschner, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; B.Y. Renard, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany. E-mail: ; M.G. Weller, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; T. Muth, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany. E-mail:
| | - Michael G Weller
- Corresponding authors: D. Beslic, Robert Koch Institute, ZKI-PH 3, Nordufer 20, 13353 Berlin, Germany. E-mail: ; G. Tscheuschner, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; B.Y. Renard, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany. E-mail: ; M.G. Weller, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; T. Muth, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany. E-mail:
| | - Thilo Muth
- Corresponding authors: D. Beslic, Robert Koch Institute, ZKI-PH 3, Nordufer 20, 13353 Berlin, Germany. E-mail: ; G. Tscheuschner, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; B.Y. Renard, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany. E-mail: ; M.G. Weller, Federal Institute for Materials Research and Testing (BAM), Richard-Willstätter-Straße 11, 12489 Berlin, Germany. E-mail: ; T. Muth, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany. E-mail:
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