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structural-biology/ai produced the result/Cell 2023 · v2

Researchers map the sixteen-part Commander complex using crystals, cryo-EM and AlphaFold2

A team assembled a complete structural model of Commander, a protein machine in cells linked to Ritscher-Schinzel syndrome, by combining X-ray crystallography and cryo-electron microscopy with AlphaFold2 Multimer predictions that filled in the parts experiments did not resolve.

1. Express, purify and crystallise Commander sub-assemblies2. Collect cryo-EM movies and X-ray diffraction data3. Train neural-network particle picker on manual picks4. Auto-pick particles with the trained network5. Classify particles and refine 3D reconstructions6. Predict subunit complexes with AlphaFold2 Multimer7. Build, refine and assemble atomic models8. Test predicted interfaces by mutagenesis and proteomics

spectrum · one line per step, placed by what the step does · bright lines used AI

Structure of the endosomal Commander complex linked to Ritscher-Schinzel syndrome
Cell, 2023

doi:10.1016/j.cell.2023.04.003 · record aix-00003 v2 · checked 2026-10-07

ai-resultrole of AI
AI was for
Structure determination, Detection
Model family
Transformer, Convolutional neural network
Checked by
Experimental
Code
not reported

The finding the paper is about came from the AI.

read as

The science is explained before the AI appears. Switch to field specialist to go straight to the method.

Assumes the discipline and goes straight to the method.

The diagram, the record and what the paper did not report are identical in both modes. Only the framing changes — never the evidence.

Introduction by AIxSci · plain language

What this research was about

Cells constantly sort the proteins sitting in their outer membrane. Some are sent off to be destroyed; others are collected up and returned to the surface for reuse. Much of this sorting happens in small internal compartments called endosomes, and it is carried out by large assemblies of proteins that recognise cargo and hand it on. Commander is one such assembly. It is built from sixteen separate protein subunits, among them a group called the COMMD proteins, two long coiled proteins named CCDC22 and CCDC93, and a three-part unit called Retriever. Faults in some of these subunits are linked to inherited conditions, including Ritscher-Schinzel syndrome and X-linked intellectual disability.

Knowing which subunit touches which, and where, is what makes a disease-causing mutation interpretable. But a sixteen-piece assembly is hard to see. Crystallography needs a well-ordered crystal, which large flexible machines rarely give; cryo-electron microscopy, which freezes copies of a complex and averages thousands of noisy images, can leave floppy regions blurred out. The researchers set out to build one joined-up model of the whole thing, using crystals for some pieces, cryo-EM maps for others, and computational prediction where neither worked.

Where AI came in

Two learned tools did distinct jobs. In the microscopy work, a neural network called Topaz was trained on particles that people had picked out of the images by hand, and the trained network then found particles across the datasets automatically, in place of further manual picking. The resulting images were classified and refined with conventional software, giving maps of the twelve-subunit core reported at 3.1 and 3.5 ångströms.

The second tool was AlphaFold2 Multimer, a program that predicts the shape of proteins, and how several of them fit together, from their amino-acid sequences alone. Run through the ColabFold interface, it produced models of the COMMD proteins with the ends of CCDC22 and CCDC93, the Retriever trimer, the link between Retriever and the core, and the site where a subunit called DENND10 binds. Those predictions were fitted into the cryo-EM maps, used as a starting template for one crystal structure, and supplied coordinates for regions the experiments left unresolved. Mutations designed from the predicted contact points were then tested in cells and in the test tube.

Written by AIxSci from the checked record below, to give context for readers outside the field. It is not part of the record.

The work

Technical · from the record

The authors determined the architecture of the sixteen-subunit endosomal Commander complex by combining X-ray crystallography, cryo-electron microscopy and AlphaFold2 Multimer structure prediction. AlphaFold2 models of the ten COMMD proteins together with the N-terminal regions of CCDC22 and CCDC93 were docked into cryo-EM maps of the twelve-subunit CCC complex, reported at 3.1 A in CryoSPARC and 3.5 A in RELION 4.0, and predictions supplied coordinates for parts the experimental data did not resolve, including the Retriever trimer, the CCDC coiled-coils that link CCC to Retriever, and the DENND10 binding site. A neural-network particle picker was trained on manually picked particles and then used to pick particles from the micrographs. The COMMD proteins were found to form a hetero-decameric ring of five specific heterodimers intercalated by CCDC22 and CCDC93 linkers, mutations designed from the predicted interfaces perturbed the corresponding interactions in cells and in vitro, and mutations causing X-linked intellectual disability and Ritscher-Schinzel syndrome were mapped onto the assembled model.

How AI was used

Two learned components were used. For cryo-EM, roughly 3,000 manually picked Retriever particles were used to train the Topaz picker implemented in RELION, and the trained network then auto-picked particles for the Retriever and CCC datasets; the picks were classified and refined with conventional CryoSPARC and RELION 4.0 processing. For structure determination, AlphaFold2 Multimer was run through the ColabFold interface on Google Colab to predict the COMMD1-10 plus CCDC22/CCDC93 N-terminal dodecamer, the Retriever VPS35L-VPS26C-VPS29 trimer, the DENND10-CCDC22-CCDC93 coiled-coil complex, and the VPS35L-CCDC interface, with iPTM scores, PAE plots and model-to-model alignment used to judge the predictions, and analogous predictions made for zebrafish and choanoflagellate sequences. The predicted dodecamer was docked into the sharpened cryo-EM map in ChimeraX and refined with PHENIX, COOT and ISOLDE; an AlphaFold2 prediction also served as the molecular-replacement template for the VPS29-VPS35L peptide crystal structure, and the machine-learning model-building program ModelAngelo was run to build a structure ab initio from the map for comparison. Predicted structures were merged into a single sixteen-subunit model, with experimentally determined regions substituted in and the result refined in PHENIX. Candidate CCC-Retriever assemblies were tested by prediction before the reported interface was selected, and point mutations designed from the models were then assayed in cells and in vitro.

The shape of the work

Structural · the record, drawn

EXPERIMENTACQUISITIONTRAININGINFERENCEPREPARATIONINFERENCEOPTIMISATIONVALIDATION12345678AIAIAIAIExpress, purifyand crystalliseCommander sub-as…Collect cryo-EMmovies and X-raydiffraction dataTrainneural-networkparticle picker …Auto-pickparticles withthe trained netw…Classifyparticles andrefine 3D recons…Predict subunitcomplexes withAlphaFold2 Multi…Build, refine andassemble atomicmodelsTest predictedinterfaces bymutagenesis and …↤ manual curation↤ manual curation↤ unresolved measurement↤ manual curation
AI stepNo AI↤ what the AI stood in for
1Experiment
no AI

Express, purify and crystallise Commander sub-assemblies

Physical execution, by hand or by robot.

Recombinant human Retriever (3xStrepII-VPS26C, VPS35L, and VPS29-6xHis) was expressed in insect cells using the biGBac system/MultiBac BEVSwhere the paper describes this · verbatim
in the paper
2Acquisition
no AI

Collect cryo-EM movies and X-ray diffraction data

Obtaining raw data, whether by measurement, download or retrieval.

Data collection of the CCC complex was performed on a 300 kV ThermoFisher Scientific Titan Krios transmission electron microscopewhere the paper describes this · verbatim
in the paper
3Training
AI

Train neural-network particle picker on manual picks

Fitting model parameters, including fine-tuning an existing model. The AI stood in for manual curation.

The manually picked particles were used to train Topaz which is implemented within RELION.where the paper describes this · verbatim
in the paper
4Inference
AI

Auto-pick particles with the trained network

Running a trained model over new data to predict, classify or score. The AI stood in for manual curation.

Particles were then auto picked using the Topaz trained network.where the paper describes this · verbatim
in the paper
5Preparation
no AI

Classify particles and refine 3D reconstructions

Cleaning, filtering, normalising or labelling data already obtained.

Data processing in CryoSPARC yielded a 3D reconstructionwhere the paper describes this · verbatim
in the paper
6Inference
AI

Predict subunit complexes with AlphaFold2 Multimer

Running a trained model over new data to predict, classify or score. The AI stood in for unresolved measurement.

Initially, a model of the dodecamer including all ten COMMD proteins and the N-terminal regions of CCDC22 and CCDC93 was constructed using AlphaFold2 multimerwhere the paper describes this · verbatim
in the paper
7Optimisation
AI

Build, refine and assemble atomic models

Iterative search over a space. The AI stood in for manual curation.

an essentially identical ab initio structure was built using the machine-learning guided modeling software Modelangelowhere the paper describes this · verbatim
in the paper
8Validation
no AI

Test predicted interfaces by mutagenesis and proteomics

Testing outputs against ground truth.

Mutations in CCDC22 and CCDC93 within the predicted binding interface either reduced or abolished the interaction to below detectable levelswhere the paper describes this · verbatim
in the paper

What the record says

Technical · every part carries its own basis

+ in the paper~ our reading− not reported

How to read the quotations. A quotation shows where the paper describes something. It does not quote every value beside it: one passage locates a part of the work, and values without their own quotation are our reading of that passage.

~Role of AI
AI produced the resultour reading

The paper's central product is a complete model of the sixteen-subunit Commander complex; AlphaFold2 Multimer models supplied the coordinates for regions not resolved experimentally (Retriever, CCDC coiled-coils, DENND10 interface, CCC-Retriever coupling) and were the basis of the docked cryo-EM model. Abstract states: 'Combining X-ray crystallography, electron cryomicroscopy, and in silico predictions, we have assembled a complete structural model of Commander.'

~What the AI was for
All protein models were generated using AlphaFold2 Multimer implemented in the ColabFold interfacewhere the paper describes this · verbatim
~Model families
~How it was taught
Zero-shotSupervisedour reading
~Models named
AlphaFold2 Multimer (ColabFold) · Off the shelfTopaz (particle picker, implemented in RELION) · Trained from scratchModelAngelo · Off the shelfour reading
+How results were checked
Experimentalin the paper
We validated the major interface by mutagenesis of key residueswhere the paper describes this · verbatim
+Code · weights · data
code not reportedweights not reporteddata not reportedin the paper
the ColabFold interface available on the Google Colab platformwhere the paper describes this · verbatim
+Compute
AlphaFold2 Multimer was run through ColabFold on the Google Colab platform, with the final Commander model compiled from three predictions each of about 2,000 amino acids because of the platform limit; no GPU hardware, accelerator time or run times are reported.in the paper

What this paper did not report

Technical · absence is published deliberately

Reported as not stated — 7 items
  • CodeWhether the code is available is not stated.
  • Trained model weightsWhether the trained model is available is not stated.
  • DataWhether the data are available is not stated.
  • How many were testedThe paper gives no count of what was tested.
  • Version of AlphaFold2 Multimer (ColabFold)Which version of the model was used is not stated.
  • Version of Topaz (particle picker, implemented in RELION)Which version of the model was used is not stated.
  • Version of ModelAngeloWhich version of the model was used is not stated.

About this article

Record aix-00003, version 2, checked by a person on 2026-10-07. The record describes the paper; it does not assess whether the paper's findings are right. The paper is published under CC-BY-4.0; quotations are at most 25 words. How we work · Report an error