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Zhen H, Liu J, Xiong K, Zheng L, Hu Y, Li M, Jin W. Engineering a carboxypeptidase from Aspergillus oryzae M30011 to improve the terminal-specific enzymatic hydrolysis of aromatic amino acids. Process Biochem 2023. [DOI: 10.1016/j.procbio.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Kiyoshi M, Caaveiro JMM, Miura E, Nagatoishi S, Nakakido M, Soga S, Shirai H, Kawabata S, Tsumoto K. Affinity improvement of a therapeutic antibody by structure-based computational design: generation of electrostatic interactions in the transition state stabilizes the antibody-antigen complex. PLoS One 2014; 9:e87099. [PMID: 24475232 PMCID: PMC3903617 DOI: 10.1371/journal.pone.0087099] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Accepted: 12/19/2013] [Indexed: 12/18/2022] Open
Abstract
The optimization of antibodies is a desirable goal towards the development of better therapeutic strategies. The antibody 11K2 was previously developed as a therapeutic tool for inflammatory diseases, and displays very high affinity (4.6 pM) for its antigen the chemokine MCP-1 (monocyte chemo-attractant protein-1). We have employed a virtual library of mutations of 11K2 to identify antibody variants of potentially higher affinity, and to establish benchmarks in the engineering of a mature therapeutic antibody. The most promising candidates identified in the virtual screening were examined by surface plasmon resonance to validate the computational predictions, and to characterize their binding affinity and key thermodynamic properties in detail. Only mutations in the light-chain of the antibody are effective at enhancing its affinity for the antigen in vitro, suggesting that the interaction surface of the heavy-chain (dominated by the hot-spot residue Phe101) is not amenable to optimization. The single-mutation with the highest affinity is L-N31R (4.6-fold higher affinity than wild-type antibody). Importantly, all the single-mutations showing increase affinity incorporate a charged residue (Arg, Asp, or Glu). The characterization of the relevant thermodynamic parameters clarifies the energetic mechanism. Essentially, the formation of new electrostatic interactions early in the binding reaction coordinate (transition state or earlier) benefits the durability of the antibody-antigen complex. The combination of in silico calculations and thermodynamic analysis is an effective strategy to improve the affinity of a matured therapeutic antibody.
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Affiliation(s)
- Masato Kiyoshi
- Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Jose M. M. Caaveiro
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Laboratory of Medical Proteomics, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Eri Miura
- Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Satoru Nagatoishi
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Makoto Nakakido
- Laboratory of Medical Proteomics, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Shinji Soga
- Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, Japan
| | - Hiroki Shirai
- Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, Japan
| | - Shigeki Kawabata
- Molecular Medicine Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, Japan
| | - Kouhei Tsumoto
- Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Laboratory of Medical Proteomics, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- * E-mail:
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Nagao C, Nagano N, Mizuguchi K. Prediction of detailed enzyme functions and identification of specificity determining residues by random forests. PLoS One 2014; 9:e84623. [PMID: 24416252 PMCID: PMC3885575 DOI: 10.1371/journal.pone.0084623] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 11/15/2013] [Indexed: 12/03/2022] Open
Abstract
Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
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Affiliation(s)
- Chioko Nagao
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
| | - Nozomi Nagano
- Computational Biology Research Center, AIST, Koto-ku, Tokyo, Japan
| | - Kenji Mizuguchi
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
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