Genetic Algorithm-Enhanced Direct Method in Protein Crystallography
Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction...
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MDPI AG
2025-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/2/288 |
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| author | Ruijiang Fu Wu-Pei Su Hongxing He |
| author_facet | Ruijiang Fu Wu-Pei Su Hongxing He |
| author_sort | Ruijiang Fu |
| collection | DOAJ |
| description | Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction failure. Here, we present an enhanced direct method incorporating genetic algorithms for electron density modification in real space. The method features customized selection, crossover, and mutation strategies; premature convergence prevention; and efficient message passing interface (MPI) parallelization. We systematically tested the method on 15 protein structures from different space groups with diffraction resolutions of 1.35∼2.5 Å. The test cases included high-solvent-content structures, high-resolution structures with medium solvent content, and structures with low solvent content and non-crystallographic symmetry (NCS). Results showed that the enhanced method significantly improved success rates from below 30% to nearly 100%, with average phase errors reduced below 40°. The reconstructed electron density maps were of sufficient quality for automated model building. This method provides an effective alternative for solving structures that are difficult to predict accurately by AlphaFold3 or challenging to solve by molecular replacement and experimental phasing methods. The implementation is available on Github. |
| format | Article |
| id | doaj-art-2eb51ef5daf44a848e64f8fd07873f7f |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-2eb51ef5daf44a848e64f8fd07873f7f2025-01-24T13:43:25ZengMDPI AGMolecules1420-30492025-01-0130228810.3390/molecules30020288Genetic Algorithm-Enhanced Direct Method in Protein CrystallographyRuijiang Fu0Wu-Pei Su1Hongxing He2Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo 315211, ChinaDepartment of Physics and Texas Center for Superconductivity, University of Houston, Houston, TX 77204, USADepartment of Physics, School of Physical Science and Technology, Ningbo University, Ningbo 315211, ChinaDirect methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction failure. Here, we present an enhanced direct method incorporating genetic algorithms for electron density modification in real space. The method features customized selection, crossover, and mutation strategies; premature convergence prevention; and efficient message passing interface (MPI) parallelization. We systematically tested the method on 15 protein structures from different space groups with diffraction resolutions of 1.35∼2.5 Å. The test cases included high-solvent-content structures, high-resolution structures with medium solvent content, and structures with low solvent content and non-crystallographic symmetry (NCS). Results showed that the enhanced method significantly improved success rates from below 30% to nearly 100%, with average phase errors reduced below 40°. The reconstructed electron density maps were of sufficient quality for automated model building. This method provides an effective alternative for solving structures that are difficult to predict accurately by AlphaFold3 or challenging to solve by molecular replacement and experimental phasing methods. The implementation is available on Github.https://www.mdpi.com/1420-3049/30/2/288direct methodgenetic algorithmprotein crystallographyphase problemnon-crystallographic symmetryparallel computing |
| spellingShingle | Ruijiang Fu Wu-Pei Su Hongxing He Genetic Algorithm-Enhanced Direct Method in Protein Crystallography Molecules direct method genetic algorithm protein crystallography phase problem non-crystallographic symmetry parallel computing |
| title | Genetic Algorithm-Enhanced Direct Method in Protein Crystallography |
| title_full | Genetic Algorithm-Enhanced Direct Method in Protein Crystallography |
| title_fullStr | Genetic Algorithm-Enhanced Direct Method in Protein Crystallography |
| title_full_unstemmed | Genetic Algorithm-Enhanced Direct Method in Protein Crystallography |
| title_short | Genetic Algorithm-Enhanced Direct Method in Protein Crystallography |
| title_sort | genetic algorithm enhanced direct method in protein crystallography |
| topic | direct method genetic algorithm protein crystallography phase problem non-crystallographic symmetry parallel computing |
| url | https://www.mdpi.com/1420-3049/30/2/288 |
| work_keys_str_mv | AT ruijiangfu geneticalgorithmenhanceddirectmethodinproteincrystallography AT wupeisu geneticalgorithmenhanceddirectmethodinproteincrystallography AT hongxinghe geneticalgorithmenhanceddirectmethodinproteincrystallography |