Machine Learning Assisted Pyrrolysyl-tRNA Synthetase (PylRS) Design

This repository encompasses all the training code, model parameters, and the necessary code for inference. It provides a replicable process for predicting non-canonical amino acid TCOY on Google Colab. The provided example code allows users to attempt other non-canonical computations, but the required cartddg calculations must be completed in an environment with Rosetta installed.

Note: UniRep model is fine-tuned specifically for PylRS, and the Autogluon model is designed for amino acids similar to tyrosine. For more general applications, additional experimental data and retraining are necessary.

1. Preparing sturcture templates

Relax the template pdb if you have one, follow the protocol from Cage-Prox

Otherwise you can predict the structure using ColabFold

2. Sequence embedding

Command line of ESM-1v

python /path/of/esm/variant-prediction/predict-multi.py \
    --model-location esm1v_t33_650M_UR90S_1 esm1v_t33_650M_UR90S_2 esm1v_t33_650M_UR90S_3 esm1v_t33_650M_UR90S_4 esm1v_t33_650M_UR90S_5 \
    --sequence SAPALTKSQTDRLEVLLNPKDEISLNSGKPFRELESELLSRRKKDLQQIYAEERENYLGKLEREITRFFVDRGFLEIKSPILIPLEYIERMGIDNDTELSKQIFRVDKNFCLRPMLAPNLYNYLRKLDRALPDPIKIFEIGPCYRKESDGKEHLEEFTMLNFCQMGSGCTRENLESIITDFLNHLGIDFKIVGDSCMVYGDTLDVMHGDLELSSAVVGPIPLDREWGIDKPWIGAGFGLERLLKVKHDFKNIKRAARSESYYNGISTNL \
    --dms-input mm_diskAB.csv \
    --mutation-col mutant \
    --dms-output mm_diskAB_1v_labeled.csv \
    --offset-idx 186 \
    --scoring-strategy wt-marginals

Command line of ESM-1b

python /path/of/esm/variant-prediction/predict-multi.py \
    --model-location esm_msa1b_t12_100M_UR50S \
    --sequence SAPALTKSQTDRLEVLLNPKDEISLNSGKPFRELESELLSRRKKDLQQIYAEERENYLGKLEREITRFFVDRGFLEIKSPILIPLEYIERMGIDNDTELSKQIFRVDKNFCLRPMLAPNLYNYLRKLDRALPDPIKIFEIGPCYRKESDGKEHLEEFTMLNFCQMGSGCTRENLESIITDFLNHLGIDFKIVGDSCMVYGDTLDVMHGDLELSSAVVGPIPLDREWGIDKPWIGAGFGLERLLKVKHDFKNIKRAARSESYYNGISTNL \
    --dms-input mm_diskAB.csv \
    --mutation-col mutant \
    --dms-output mm_diskAB_1b_labeled.csv \
    --offset-idx 186 \
    --scoring-strategy masked-marginals \
    --msa-path mm-hhb.a3m

Command line of eUnirep

#cd unirep/
python /path/of/jax-unirep/scripts/run_jax_evotuning.py \
    outputs/PylRS_train_set.fasta.txt \
    outputs/PylRS_out_domain_val_set.fasta.txt 256

3. Preparing input data (3.training)

Generate confs.sdf using RDKit and refine

python gen_rotamers.py UAA-info.csv
cd U[X]
  python ../refine_rotamers.py confs.sdf
cd ..

Generate rotlib

./run_all_protocol.sh UA UB [...]

Generate charge using QM

4. Prediction

Follow the same data preparing protocol above.

#TZ with R and S type
python concat_data_for_prediction.py Z R S
#TCOY with ax and eq type
python concat_data_for_prediction.py X ax eq

Predict and rank for each single mutation, see notbook predict-ssm.ipynb.