Counteraction between Astrin-PP1 and Cyclin-B-CDK1 pathways protects chromosome-microtubule attachments independent of biorientation

Defects in chromosome-microtubule attachment can cause chromosomal instability (CIN), frequently associated with infertility and aggressive cancers. Chromosome-microtubule attachment is mediated by a large macromolecular structure, the kinetochore. Sister kinetochores of each chromosome are pulled by microtubules from opposing spindle-poles, a state called biorientation which prevents chromosome missegregation. Kinetochore-microtubule attachments that lack the opposing-pull are detached by Aurora-B/Ipl1. It is unclear how mono-oriented attachments that precede biorientation are spared despite the lack of opposing-pull. Using an RNAi-screen, we uncover a unique role for the Astrin-SKAP complex in protecting mono-oriented attachments. We provide evidence of domains in the microtubule-end associated protein that sense changes specific to end-on kinetochore-microtubule attachments and assemble an outer-kinetochore crescent to stabilise attachments. We find that Astrin-PP1 and Cyclin-B-CDK1 pathways counteract each other to preserve mono-oriented attachments. Thus, CIN prevention pathways are not only surveying attachment defects but also actively recognising and stabilising mature attachments independent of biorientation.

Detecting ice artefacts in processed macromolecular diffraction data with machine learning

Contamination with diffraction from ice crystals can negatively affect, or even impede macromolecular structure determination and therefore, detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed, it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX webserver and CCP4-distributed software.

A flexible framework for multi-particle refinement in cryo-electron tomography

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are increasingly used for macromolecular structure determination in situ. Here, we introduce a set of computational tools and resources designed to enable flexible approaches to STA through increased automation and simplified metadata handling. We create a bidirectional interface between the Dynamo software package and the Warp-Relion-M pipeline, providing a framework for ab initio and geometrical approaches to multiparticle refinement in M. We illustrate the power of working within this framework by applying it to EMPIAR-10164, a publicly available dataset containing immature HIV-1 virus-like particles (VLPs), and a challenging in situ dataset containing chemosensory arrays in bacterial minicells. Additionally, we provide a comprehensive, step-by-step guide to obtaining a 3.4-Å reconstruction from EMPIAR-10164. The guide is hosted on https://teamtomo.org/, a collaborative online platform we establish for sharing knowledge about cryo-ET.

New restraints and validation approaches for nucleic acid structures in PDB-REDO

The quality of macromolecular structure models crucially depends on refinement and validation targets, which optimally describe the expected chemistry. Commonly used software for these two procedures has been designed and developed in a protein-centric manner, resulting in relatively few established features for the refinement and validation of nucleic acid-containing structure models. Here, new nucleic acid-specific approaches implemented in PDB-REDO are described, including a new restraint model using noncovalent geometries (base-pair hydrogen bonding and base-pair stacking) as refinement targets. New validation routines are also presented, including a metric for Watson–Crick base-pair geometry normality (Z bpG). Applying the PDB-REDO pipeline with the new restraint model to the whole Protein Data Bank (PDB) demonstrates an overall positive effect on the quality of nucleic acid-containing structure models. Finally, we discuss examples of improvements in the geometry of specific nucleic acid structures in the PDB. The new PDB-REDO models and pipeline are available at https://pdb-redo.eu/.