> 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/latent-state-of-a-human-serpin-with-msa-subsampling.md). # Latent State of a Human Serpin with MSA Subsampling This tutorial walks through the reproduction of **Figure S7** from our paper: **"Synergistic Integration of MSA Subsampling with Gradient-Based Optimization"** We demonstrate how ROCKET enables recovery of the **latent conformation** of the human serpin **PAI-1 (PDB ID 1LJ5)** using MSA subsampling and scoring-based selection of MSA subsamples, followed by gradient-based refinement. The AlphaFold2 prediction for this serpin from the full MSA yields its **metastable active conformation**. The experimental structure instead captures its **latent state** (PDB ID 1LJ5). This example shows how to run MSA subsampling with `rk.msacluster`, score the resulting predictions with `rk.score`, and then refine the best start with `rk.refine`.

The latent state conformation (PDB ID 1LJ5) is not accessible by gradient descent alone when ROCKET refinement is started from the full MSA prediction.

### 1. Collect the required files We have prepared ROCKET inputs of this serpin case for download at . Download and decompress it: ```bash tar xvf 1lj5_Human_Serpin_Tutorial.tar.gz ``` You will see a folder organized in the following manner: ``` data_for_1lj5/ ├── 1lj5_data ├── 1lj5_fasta ├── 1lj5_preprocessing_outputs ├── alignments └── preprocess.sh ``` For reproducibility, we have prepared all the necessary files in `1lj5_preprocessing_outputs`. If you want to generate them from scratch, follow [Launch with Your Own X-ray Data](/rocket-docs/launch-with-your-own-x-ray-data.md) and [rk.preprocess](/rocket-docs/api/rk.preprocess.md). ### 2. Subsample the MSA Activate the environment with `ROCKET` installed. Then change into `data_for_1lj5/1lj5_preprocessing_outputs` and run: ```bash rk.msacluster \ subv1 \ -i ./alignments \ -o ./serpin_clusters \ --n_controls 30 \ --run_TSNE ``` This will subsample and make a `serpin_clusters/` folder with subsampled `*.a3m` files. Files starting with `subv1_` are clustered subsamples with DBSCAN; those starting with `U10` or `U100` are uniformly random sampled MSAs with 10 or 100 sequences. ### 3. Score the different MSA subsamples against the experimental data Keep working in `1lj5_preprocessing_outputs`. Then run: ```bash rk.score \ . \ 1lj5 \ -i serpin_clusters/ \ -o serpin_scores/ \ --init_recycling 20 \ --score_fullmsa \ --datamode xray ``` This may take some time, depending on how many subsampled MSAs you score. The default `--init_recycling` value in ROCKET is `4`, which is a decent speed and quality compromise. For the serpin case in the paper, we used `20`, which matches the original AF2 default. After the run, you should see a `serpin_scores/` folder with: * A CSV file with experimental scores, mean pLDDT, MSA depth, and R-factors for each prediction. * The prediction PDB files for each subsampled MSA. ``` serpin_scores/ ├── fullmsa_postRBR.pdb ├── msa_scoring.csv ├── U10-subv1_000_postRBR.pdb └── U10-subv1_001_postRBR.pdb ... ``` We can identify a cluster that scores highly in LLG and is indeed a closer conformation to the experimentally resolved one:

Ranking predictions from MSA subsamples by their experimental likelihoods identifies a better starting model for gradient-based refinement (samples are ordered along the x-axis by increasing gains in experimental likelihood), unlike pLDDT-based scoring, which does not (highest samples along the y-axis do not resemble the experimental conformation).

**Note:** The results from `rk.msacluster` and `rk.score` are somewhat stochastic. Random seeds in subsampling and rigid-body refinement both contribute. Your `msa_scoring.csv` will not match ours exactly. Still, we consistently see a prediction with an inserted loop among the top results across different seeds. ### 4. Further improve the structure with gradient-driven refinement For reproducibility, we attached the subsampled MSAs and scores from our run in `repro_msaclusters/` and `repro_msascore/`. Among these, `v1_034.a3m` had the highest LLG score. We therefore use the `v1_034.a3m` prediction for further gradient-driven refinement in `1lj5_preprocessing_outputs`: ```bash rk.refine refine_config.yml ``` We include the config file `refine_config.yml` in the zenodo folder, with settings below: ```yaml note: phase1_v1_034 data: datamode: xray free_flag: R-free-flags testset_value: 1 min_resolution: 4.0 max_resolution: null voxel_spacing: 8.0 msa_subratio: null w_plddt: 0.0 downsample_ratio: null paths: path: ./ file_id: 1lj5 input_pdb: ./ROCKET_inputs/1lj5-pred-aligned.pdb template_pdb: null input_msa: serpin_clusters/v1_034.a3m sub_msa_path: null sub_delmat_path: null msa_feat_init_path: null starting_bias: null starting_weights: null uuid_hex: null execution: cuda_device: 0 num_of_runs: 3 verbose: false algorithm: bias_version: 3 iterations: 100 init_recycling: 20 domain_segs: null optimization: additive_learning_rate: 0.05 multiplicative_learning_rate: 1.0 weight_decay: 0.0001 batch_sub_ratio: 0.7 number_of_batches: 1 rbr_opt_algorithm: lbfgs rbr_lbfgs_learning_rate: 150.0 smooth_stage_epochs: 50 phase2_final_lr: 0.001 l2_weight: 0.001 features: solvent: true sfc_scale: true 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: false wandb_project: null wandb_entity: null wandb_name: null wandb_tags: null wandb_notes: null ``` The refinement results will be saved in `ROCKET_outputs`. We also include the refinement trajectory from our previous run in `ROCKET_outputs/e80938425e`.

Gradient-based refinement of the best starting prediction from the MSA subsamples results in a structure that closely resembles the latent conformation.

### Finalize geometry and B-factors We always append a brief standard refinement run afterwards. We used `phenix.refine` in the paper. This helps refine B-factors and polish geometry on the model that comes straight out of ROCKET.