ArticleActa crystallographica. Section D, Structural biology2022
MrParse: finding homologues in the PDB and the EBI AlphaFold database for molecular replacement and more.
Article in Acta crystallographica. Section D, Structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Slice'N'Dice: maximizing the value of predicted models for structural biologists.Acta crystallographica. Section D, Structural biology · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- ABCFold: easier running and comparison of AlphaFold 3, Boltz-1, and Chai-1.Bioinformatics advances · 2025Article
- NucleoFind: a deep-learning network for interpreting nucleic acid electron density.Nucleic acids research · 2024Article
- AlphaFold two years on: Validation and impact.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.BMC bioinformatics · 2024Article
- Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure.Biophysical journal · 2024Review
- Predicted models and CCP4.Acta crystallographica. Section D, Structural biology · 2023Article
- The CCP4 suite: integrative software for macromolecular crystallography.Acta crystallographica. Section D, Structural biology · 2023Article
- Structure of reverse gyrase with a minimal latch that supports ATP-dependent positive supercoiling without specific interactions with the topoisomerase domain.Acta crystallographica. Section D, Structural biology · 2023Article
- CCP4 Cloud for structure determination and project management in macromolecular crystallography.Acta crystallographica. Section D, Structural biology · 2022Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
Crystallographers have an array of search-model options for structure solution by molecular replacement (MR). The well established options of homologous experimental structures and regular secondary-structure elements or motifs are increasingly supplemented by computational modelling. Such modelling may be carried out locally or may use pre-calculated predictions retrieved from databases such as the EBI AlphaFold database. MrParse is a new pipeline to help to streamline the decision process in MR by consolidating bioinformatic predictions in one place. When reflection data are provided, MrParse can rank any experimental homologues found using eLLG, which indicates the likelihood that a given search model will work in MR. Inbuilt displays of predicted secondary structure, coiled-coil and transmembrane regions further inform the choice of MR protocol. MrParse can also identify and rank homologues in the EBI AlphaFold database, a function that will also interest other structural biologists and bioinformaticians.
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