Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I
Abstract Background All chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular S...
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2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06038-y |
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| author | Lujun Luo Tarikul I. Milon Elijah K. Tandoh Walter J. Galdamez Andrei Y. Chistoserdov Jianping Yu Jan Kern Yingchun Wang Wu Xu |
| author_facet | Lujun Luo Tarikul I. Milon Elijah K. Tandoh Walter J. Galdamez Andrei Y. Chistoserdov Jianping Yu Jan Kern Yingchun Wang Wu Xu |
| author_sort | Lujun Luo |
| collection | DOAJ |
| description | Abstract Background All chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular Spatial Relationship (TSR)-based algorithm converts 3D structures into integers (TSR keys). A comprehensive study was conducted, by taking advantage of the PS I 3D structures and the TSR-based algorithm, to answer three questions: (i) Are electron cofactors including P700, A-1 and A0, which are chemically identical chlorophylls, structurally different? (ii) There are two electron transfer chains (A and B branches) in PS I. Are the cofactors on both branches structurally different? (iii) Are the amino acids in cofactor binding sites structurally different from those not in cofactor binding sites? Results The key contributions and important findings include: (i) a novel TSR-based method for representing 3D structures of pigments as well as for quantifying pigment structures was developed; (ii) the results revealed that the redox cofactor, P700, are structurally conserved and different from other redox factors. Similar situations were also observed for both A-1 and A0; (iii) the results demonstrated structural differences between A and B branches for the redox cofactors P700, A-1, A0 and A1 as well as their cofactor binding sites; (iv) the tryptophan residues close to A0 and A1 are structurally conserved; (v) The TSR-based method outperforms the Root Mean Square Deviation (RMSD) and the Ultrafast Shape Recognition (USR) methods. Conclusions The structural analyses of redox cofactors and their binding sites provide a foundation for understanding the unique chemical and physical properties of each redox cofactor in PS I, which are essential for modulating the rate and direction of energy and electron transfers. |
| format | Article |
| id | doaj-art-50d0fd239ddf4e2bb06e8a794fed7c19 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-50d0fd239ddf4e2bb06e8a794fed7c192025-01-19T12:40:55ZengBMCBMC Bioinformatics1471-21052025-01-0126113310.1186/s12859-025-06038-yDevelopment of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem ILujun Luo0Tarikul I. Milon1Elijah K. Tandoh2Walter J. Galdamez3Andrei Y. Chistoserdov4Jianping Yu5Jan Kern6Yingchun Wang7Wu Xu8Department of Chemistry, University of Louisiana at LafayetteDepartment of Chemistry, University of Louisiana at LafayetteDepartment of Chemistry, University of Louisiana at LafayetteDepartment of Chemistry, University of Louisiana at LafayetteDepartment of Biology, University of Louisiana at LafayetteBiosciences Center, National Renewable Energy LaboratoryBioenergetics Department, MBIB Division, Lawrence Berkeley National LaboratoryInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesDepartment of Chemistry, University of Louisiana at LafayetteAbstract Background All chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular Spatial Relationship (TSR)-based algorithm converts 3D structures into integers (TSR keys). A comprehensive study was conducted, by taking advantage of the PS I 3D structures and the TSR-based algorithm, to answer three questions: (i) Are electron cofactors including P700, A-1 and A0, which are chemically identical chlorophylls, structurally different? (ii) There are two electron transfer chains (A and B branches) in PS I. Are the cofactors on both branches structurally different? (iii) Are the amino acids in cofactor binding sites structurally different from those not in cofactor binding sites? Results The key contributions and important findings include: (i) a novel TSR-based method for representing 3D structures of pigments as well as for quantifying pigment structures was developed; (ii) the results revealed that the redox cofactor, P700, are structurally conserved and different from other redox factors. Similar situations were also observed for both A-1 and A0; (iii) the results demonstrated structural differences between A and B branches for the redox cofactors P700, A-1, A0 and A1 as well as their cofactor binding sites; (iv) the tryptophan residues close to A0 and A1 are structurally conserved; (v) The TSR-based method outperforms the Root Mean Square Deviation (RMSD) and the Ultrafast Shape Recognition (USR) methods. Conclusions The structural analyses of redox cofactors and their binding sites provide a foundation for understanding the unique chemical and physical properties of each redox cofactor in PS I, which are essential for modulating the rate and direction of energy and electron transfers.https://doi.org/10.1186/s12859-025-06038-yTSR-based methodPhotosystem IRepresentation of cofactor 3D structuresCofactor and protein interactionCofactor binding site and A and B branches |
| spellingShingle | Lujun Luo Tarikul I. Milon Elijah K. Tandoh Walter J. Galdamez Andrei Y. Chistoserdov Jianping Yu Jan Kern Yingchun Wang Wu Xu Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I BMC Bioinformatics TSR-based method Photosystem I Representation of cofactor 3D structures Cofactor and protein interaction Cofactor binding site and A and B branches |
| title | Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I |
| title_full | Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I |
| title_fullStr | Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I |
| title_full_unstemmed | Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I |
| title_short | Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I |
| title_sort | development of a tsr based method for understanding structural relationships of cofactors and local environments in photosystem i |
| topic | TSR-based method Photosystem I Representation of cofactor 3D structures Cofactor and protein interaction Cofactor binding site and A and B branches |
| url | https://doi.org/10.1186/s12859-025-06038-y |
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