<i>Slice'N'Dice</i> : maximizing the value of predicted models for structural biologists

2025/02/17 by Adam J. Simpkin, Luc G. Elliot, A. W. Joseph +8 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Machine Learning in Materials Science #RNA modifications and cancer

paper · doi:10.1107/s2059798325001251

openalex publication_date 2025/02/17 · openalex created_date 2025/02/21 · openalex updated_date 2026/07/23

Abstract

With the advent of next-generation modelling methods, such as AlphaFold 2, structural biologists are increasingly using predicted structures to obtain structure solutions via molecular replacement (MR) or model fitting in single-particle cryogenic sample electron microscopy (cryoEM). Differences between the domain–domain orientations represented in a predicted model and a crystal structure are often a key limitation when using predicted models. Slice'N'Dice is a software package designed to address this issue by first slicing models into distinct structural units and then automatically placing the slices using either Phaser , MOLREP or PowerFit . The slicing step can use the AlphaFold predicted aligned error (PAE) or can operate via a variety of C α -atom-based clustering algorithms, extending the applicability to structures of any origin. The number of splits can either be selected by the user or determined automatically. Slice'N'Dice is available for both MR and automated map fitting in the CCP 4 and CCP-EM software suites.

Citations

• Fourier shell correlation threshold criteria
• Least squares quantization in PCM
• <i>Phaser</i>crystallographic software
• <scp>UCSF ChimeraX</scp>: Structure visualization for researchers, educators, and developers
• RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences
• Highly accurate protein structure prediction with AlphaFold
• Accurate prediction of protein structures and interactions using a three-track neural network
• КОМПЬЮТЕРНАЯ ТОМОГРАФИЯ – ОСНОВА ОПТИМИЗАЦИИ МИНИТОРАКОТОМНОГО ДОСТУПА
• ColabFold: making protein folding accessible to all
• Evolutionary-scale prediction of atomic-level protein structure with a language model
• The <i>CCP</i> 4 suite: integrative software for macromolecular crystallography
• AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
• Accurate structure prediction of biomolecular interactions with AlphaFold 3

Cited by

• Categorizing prediction modes within low-pLDDT regions of <i>AlphaFold</i> 2 structures: near-predictive, pseudostructure and barbed wire

Discussions

• Slice'N'Dice can address cases where the conformation of a structure prediction, especially in terms of inter-domain orientations, differs significantly from that of the target in both #crystallograph [bsky, 1 points, 0 comments]

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• AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network
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