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De Souza TPP, Cantão LXS, Rodrigues MQRB, Gonçalves DB, Nagem RAP, Rocha REO, Godoi RR, Lima WJN, Galdino AS, Minardi RCDM, Lima LHFD. Glycosylation and charge distribution orchestrates the conformational ensembles of a biotechnologically promissory phytase in different pHs - a computational study. J Biomol Struct Dyn 2024; 42:5030-5041. [PMID: 37325852 DOI: 10.1080/07391102.2023.2223685] [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: 03/27/2023] [Accepted: 06/06/2023] [Indexed: 06/17/2023]
Abstract
Phytases [myo-inositol(1,2,3,4,5,6) hexakisphosphate phosphohydrolases] are phytate-specific phosphatases not present in monogastric animals. Nevertheless, they are an essential supplement to feeding such animals and for human special diets. It is crucial, hence, the biotechnological use of phytases with intrinsic stability and activity at the acid pHs from gastric environments. Here we use Metadynamics (METADY) simulations to probe the conformational space of the Aspergillus nidulans phytase and the differential effects of pH and glycosylation in this same space. The results suggest that strategic combinations of pH and glycosylation affect the stability of native-like conformations and alternate these structures from a metastable to a stable profile. Furthermore, the protein segments previously reported as more thermosensitive in phytases from this family present a pivotal role in the conformational changes at different conditions, especially H2, H5-7, L8, L10, L12, and L17. Also, the glycosylations and the pH-dependent charge balance modulate the mobility and interactions at these same regions, with consequences for the surface solvation and active site exposition. Finally, although the glycosylations have stabilized the native structure and improved the substrate docking at all the studied pHs, the data suggest a higher phytate receptivity at catalytic poses for the unglycosylated structure at pH 6.5 and the glycosylated one at pH 4.5. This behavior agrees with the exact change in optimum pH reported for this enzyme, expressed on low or high glycosylating systems. We hope the results and insights presented here will be helpful in future approaches for rational engineering of technologically promising phytases and intelligent planning of their heterologous expression systems and conditions for use.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Thaís P P De Souza
- Microbial Biotechnology Laboratory, Universidade Federal de São João Del-Rei, Divinópolis, Minas Gerais, Brazil
| | - Letícia Xavier Silva Cantão
- Laboratory of Bioinformatics and Systems (LBS), Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Daniel Bonoto Gonçalves
- Department of Biosystems Engineering, Universidade Federal de São João Del-Rei, São João Del-Rei, Minas Gerais, Brazil
| | - Ronaldo Alves Pinto Nagem
- Institute of Biological Sciences Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Rafael Eduardo Oliveira Rocha
- Laboratory of Bioinformatics and Systems (LBS), Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratory Of Molecular Modeling and Bioinformatics, Department of Exacts and Biological Sciences (DECEB), Universidade Federal de São João Del-Rei, Sete Lagoas, Minas Gerais, Brazil
| | - Renato Ramos Godoi
- Institute of Biological Sciences Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - William James Nogueira Lima
- Institute of Agricultural Sciences, Universidade Federal de Minas Gerais, Campus Regional de Montes Claros, Montes Claros, Minas Gerais, Brazil
| | - Alexsandro Sobreira Galdino
- Microbial Biotechnology Laboratory, Universidade Federal de São João Del-Rei, Divinópolis, Minas Gerais, Brazil
| | - Raquel Cardoso de Melo Minardi
- Laboratory of Bioinformatics and Systems (LBS), Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Leonardo Henrique França de Lima
- Laboratory Of Molecular Modeling and Bioinformatics, Department of Exacts and Biological Sciences (DECEB), Universidade Federal de São João Del-Rei, Sete Lagoas, Minas Gerais, Brazil
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