AI-based quality assessment methods for protein structure models from cryo-EM
Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, with an increasing number of structures being determined by cryo-EM each year, many at higher resolutions. However, challenges remain in accurately interpreting cryo-EM maps. Inaccuracies can arise in regions of locally l...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Current Research in Structural Biology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665928X25000017 |
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| author | Han Zhu Genki Terashi Farhanaz Farheen Tsukasa Nakamura Daisuke Kihara |
| author_facet | Han Zhu Genki Terashi Farhanaz Farheen Tsukasa Nakamura Daisuke Kihara |
| author_sort | Han Zhu |
| collection | DOAJ |
| description | Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, with an increasing number of structures being determined by cryo-EM each year, many at higher resolutions. However, challenges remain in accurately interpreting cryo-EM maps. Inaccuracies can arise in regions of locally low resolution, where manual model building is more prone to errors. Validation scores for structure models have been developed to assess both the compatibility between map density and the structure, as well as the geometric and stereochemical properties of protein models. Recent advancements have introduced artificial intelligence (AI) into this field. These emerging AI-driven tools offer unique capabilities in the validation and refinement of cryo-EM-derived protein atomic models, potentially leading to more accurate protein structures and deeper insights into complex biological systems. |
| format | Article |
| id | doaj-art-d406bae8e4984fecbac9134100da0e7f |
| institution | Kabale University |
| issn | 2665-928X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Structural Biology |
| spelling | doaj-art-d406bae8e4984fecbac9134100da0e7f2025-02-08T05:01:09ZengElsevierCurrent Research in Structural Biology2665-928X2025-06-019100164AI-based quality assessment methods for protein structure models from cryo-EMHan Zhu0Genki Terashi1Farhanaz Farheen2Tsukasa Nakamura3Daisuke Kihara4Department of Computer Science, Purdue University, West Lafayette, IN, USADepartment of Biological Sciences, Purdue University, West Lafayette, IN, USADepartment of Computer Science, Purdue University, West Lafayette, IN, USADepartment of Biological Sciences, Purdue University, West Lafayette, IN, USA; Structural Biology Research Center, High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki, 305-0801, JapanDepartment of Computer Science, Purdue University, West Lafayette, IN, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Corresponding author. Department of Computer Science, Purdue University, West Lafayette, IN, USA.Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, with an increasing number of structures being determined by cryo-EM each year, many at higher resolutions. However, challenges remain in accurately interpreting cryo-EM maps. Inaccuracies can arise in regions of locally low resolution, where manual model building is more prone to errors. Validation scores for structure models have been developed to assess both the compatibility between map density and the structure, as well as the geometric and stereochemical properties of protein models. Recent advancements have introduced artificial intelligence (AI) into this field. These emerging AI-driven tools offer unique capabilities in the validation and refinement of cryo-EM-derived protein atomic models, potentially leading to more accurate protein structures and deeper insights into complex biological systems.http://www.sciencedirect.com/science/article/pii/S2665928X25000017Cryo-electron microscopyCryo-EMStructure modelingStructural biologyModel quality assessmentModel validation |
| spellingShingle | Han Zhu Genki Terashi Farhanaz Farheen Tsukasa Nakamura Daisuke Kihara AI-based quality assessment methods for protein structure models from cryo-EM Current Research in Structural Biology Cryo-electron microscopy Cryo-EM Structure modeling Structural biology Model quality assessment Model validation |
| title | AI-based quality assessment methods for protein structure models from cryo-EM |
| title_full | AI-based quality assessment methods for protein structure models from cryo-EM |
| title_fullStr | AI-based quality assessment methods for protein structure models from cryo-EM |
| title_full_unstemmed | AI-based quality assessment methods for protein structure models from cryo-EM |
| title_short | AI-based quality assessment methods for protein structure models from cryo-EM |
| title_sort | ai based quality assessment methods for protein structure models from cryo em |
| topic | Cryo-electron microscopy Cryo-EM Structure modeling Structural biology Model quality assessment Model validation |
| url | http://www.sciencedirect.com/science/article/pii/S2665928X25000017 |
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