> For the complete documentation index, see [llms.txt](https://rocket-9.gitbook.io/rocket-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://rocket-9.gitbook.io/rocket-docs/low-resolution-groel-sub-tomogram-average.md). # Low Resolution GroEL Sub-Tomogram Average This tutorial walks through refinement of the *E. coli* groEL chain H (PDB ID 8P4P) from **Figure 3** in our paper: **"Extracting Information from Low Resolution Data"** The goal is to show how ROCKET can build a chain into a cryoEM map that is especially low resolution or noisy.

Two distinct subunit conformations can be observed in the GroEL heptameric rings. Here we focus on refinement of chain H as an example of the conformation in the bottom ring.

### 1. Collect the required files We have prepared ROCKET inputs for download at . Download and decompress the file: ```bash tar xvf data_for_8p4pH.tar.gz ``` You will see a folder organized in the following manner: ``` data_for_8p4pH/ ├── 8p4pH_data │ ├── 8p4pH_docked.pdb │ ├── 8p4pH-fixed-model-forchainH.pdb │ ├── emd_17425_half_map_1.map │ └── emd_17425_half_map_2.map ├── 8p4pH_fasta │ └── 8p4pH.fasta ├── 8p4pH_preprocessing_outputs │ ├── 8p4pH.fasta │ ├── alignments │ ├── docking_outputs │ ├── predictions │ ├── processed_predicted_files │ ├── ROCKET_config_phase1.yaml │ ├── ROCKET_config_phase2.yaml │ ├── ROCKET_refine.yaml │ ├── ROCKET_inputs │ └── timings.json ├── alignments │ └── 8p4pH └── preprocess.sh ``` For reproducibility, we have prepared all the necessary files in `8p4pH_preprocessing_outputs`. If you want to generate them from scratch, follow [Launch with Your Own Cryo-EM Data](/rocket-docs/launch-with-your-own-cryo-em-data.md) and [rk.preprocess](/rocket-docs/api/rk.preprocess.md). This example uses half maps, which are preferred for our error model. If you already have a predocked model for your own system and only one post-processed map, `rk.preprocess` can take `--map` alone. Keep in mind that does not work when ROCKET still needs to search for the docked placement. ### 2. Run refinement The preprocessing step generates two YAML files for `rk.refine`. You can run refinement inside `8p4pH_preprocessing_outputs` with: ```bash rk.refine ROCKET_refine.yaml ``` The settings used in this example are: ```yaml note: phase1_8p4pH_refine data: datamode: cryoem free_flag: R-free-flags testset_value: 1 min_resolution: 3.0 max_resolution: null voxel_spacing: 4.5 msa_subratio: null w_plddt: 0.0 downsample_ratio: null paths: path: ./ file_id: 8p4pH input_pdb: ./ROCKET_inputs/8p4pH-pred-aligned.pdb template_pdb: null input_msa: null sub_msa_path: null sub_delmat_path: null msa_feat_init_path: null starting_bias: null starting_weights: null uuid_hex: tutorial execution: cuda_device: 0 num_of_runs: 1 verbose: false algorithm: bias_version: 3 iterations: 300 init_recycling: 20 domain_segs: null optimization: additive_learning_rate: 0.0001 multiplicative_learning_rate: 0.001 weight_decay: 0.0001 batch_sub_ratio: 1.0 number_of_batches: 1 rbr_opt_algorithm: lbfgs rbr_lbfgs_learning_rate: 1.0 smooth_stage_epochs: 50 phase2_final_lr: 0.0001 l2_weight: 1.0e-12 features: solvent: true sfc_scale: false refine_sigmaA: true additional_chain: false total_chain_copy: 1.0 bias_from_fullmsa: false chimera_profile: false alphafold: use_deepspeed_evo_attention: true monitoring: use_wandb: true wandb_project: "rocket" wandb_entity: wandb_name: "8p4pH_refine" wandb_tags: ["8p4pH", "refine"] wandb_notes: "8p4pH refinement" ``` Note that our learning rate defaults for low resolution cryo-EM are lower. The refinement trajectory will be saved to `ROCKET_outputs`. ### 3. Find the best-scoring model ROCKET will highlight the best scoring model during its refinement trajectory and save the final MSA cluster profile bias and weight tensors as `best_feat_weights_H_{best_iteration}.pt` and `best_msa_bias_H_{best_iteration}.pt` that can be used to re-predict the conformation found. The `postRBR_{best_iteration}.pdb` file can also be accessed directly in the output folder.

ROCKET refines chain H into density

### 4. Finalize geometry and B-factors This will have less of an effect at low resolution, but we recommend a brief standard run of refinement (`phenix.refine` used in the paper) to refine B-factors and polish the geometry of the best scoring model coming straight out of `ROCKET` . ### Note There is some stochasticity in the gradient descent and per-iteration rigid-body refinement steps. Two ROCKET runs started from the same inputs and parameters may still differ slightly. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://rocket-9.gitbook.io/rocket-docs/low-resolution-groel-sub-tomogram-average.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.