--- base_model: - genbio-ai/AIDO.Protein-16B license: other --- # Protein Inverse Folding We finetune the [AIDO.Protein-16B](https://huggingface.co/genbio-ai/AIDO.Protein-16B) model with LoRA on the [CATH 4.2](https://pubmed.ncbi.nlm.nih.gov/9309224/) benmark dataset. We use the same train, validation, and test splits used by the previous studies, such as [LM-Design](https://arxiv.org/abs/2302.01649), and [DPLM](https://arxiv.org/abs/2402.18567). Current version of ModelGenerator contains the inference pipeline for protein inverse folding. Experimental pipeline on other datasets (both training and testing) will be included in the future. #### Setup: Install [ModelGenerator](https://github.com/genbio-ai/modelgenerator). - It is **required** to use [docker](https://www.docker.com/101-tutorial/) to run our inverse folding pipeline. - Please set up a docker image using our provided [Dockerfile](https://github.com/genbio-ai/ModelGenerator/blob/main/Dockerfile) and run the inverse folding inference from within the docker container. - Here is an example bash script to set up and access a docker container: ``` # clone the ModelGenerator repository git clone https://github.com/genbio-ai/ModelGenerator.git # cd to "ModelGenerator" folder where you should find the "Dockerfile" cd ModelGenerator # create a docker image docker build -t aido . # create a local folder as ModelGenerator's data directory mkdir -p $HOME/mgen_data # run a container docker run -d --runtime=nvidia -it -v "$(pwd):/workspace" -v "$HOME/mgen_data:/mgen_data" aido /bin/bash # find the container ID docker ps # this will print the running containers and their IDs # execute the container with ID= docker exec -it /bin/bash # now you should be inside the docker container # test if you can access the nvidia GPUs nvidia-smi # this should print the GPUs' details ``` - Execute the following steps from **within** the docker container you just created. #### Download model checkpoints: - Download all the 15 model checkpoint chunks (named as `chunk_.bin`) from [here](https://huggingface.co/genbio-ai/AIDO.ProteinIF-16B/tree/main). Place them inside the directory `${MGEN_DATA_DIR}/modelgenerator/huggingface_models/protein_inv_fold/AIDO.ProteinIF-16B/model_chunks`. **Alternatively**, you can simply run the following script to do this (Note: this script uses the [wget](https://www.gnu.org/software/wget/) tool): ``` mkdir -p ${MGEN_DATA_DIR}/modelgenerator/huggingface_models/protein_inv_fold/AIDO.ProteinIF-16B/model_chunks bash download_model_chunks.sh ${MGEN_DATA_DIR}/modelgenerator/huggingface_models/protein_inv_fold/AIDO.ProteinIF-16B/model_chunks ``` #### Download data: - Download the preprocessed CATH 4.2 dataset from [here](https://huggingface.co/datasets/genbio-ai/protein-inverse-folding/tree/main/cath-4.2). You should find two files named [chain_set_map.pkl](https://huggingface.co/datasets/genbio-ai/protein-inverse-folding/blob/main/cath-4.2/chain_set_map.pkl) and [chain_set_splits.json](https://huggingface.co/datasets/genbio-ai/protein-inverse-folding/blob/main/cath-4.2/chain_set_splits.json). Place them inside the directory `${MGEN_DATA_DIR}/modelgenerator/datasets/protein_inv_fold/cath_4.2/`. (Note that it was originally preprocessed by [Generative Models for Graph-Based Protein Design (Ingraham et al, NeurIPS'19)](https://papers.nips.cc/paper_files/paper/2019/file/f3a4ff4839c56a5f460c88cce3666a2b-Paper.pdf), and we further preprocessed it to suit our pipeline.) **Alternatively**, you can do it by simply running the following script: ``` mkdir -p ${MGEN_DATA_DIR}/modelgenerator/datasets/protein_inv_fold/cath_4.2/ wget -P ${MGEN_DATA_DIR}/modelgenerator/datasets/protein_inv_fold/cath_4.2/ https://huggingface.co/datasets/genbio-ai/protein-inverse-folding/resolve/main/cath-4.2/chain_set_map.pkl wget -P ${MGEN_DATA_DIR}/modelgenerator/datasets/protein_inv_fold/cath_4.2/ https://huggingface.co/datasets/genbio-ai/protein-inverse-folding/resolve/main/cath-4.2/chain_set_splits.json ``` #### Run inference: - Then run the bash script for inference: ``` bash prot_inverse_folding.sh ``` - **Note:** Multi-GPU inference for inverse folding is not currently supported and will be included in the future. #### Outputs: - The evaluation score will be printed on the console. - The generated sequences will be stored the folder `proteinIF_outputs/`. There will be two output files: - **`./proteinIF_outputs/designed_sequences.pkl`**: This file will contain the raw token (amino-acid) IDs of the ground truth sequences (`"true_seq"`) and predicted sequences by our method (`"pred_seq"`), stored as numpy arrays. An example: ``` { 'true_seq': [ array([[ 4, 8, 4, 3, 12, 5, 2, 11, 16, 15, 5, 1, 11, ...]]), ... ], 'pred_seq': [ array([[ 8, 2, 4, 3, 10, 6, 2, 11, 16, 15, 6, 1, 11, ...]]), ... ] } ``` - **`./proteinIF_outputs/results_acc_.txt`** (where median accuracy is the median accuracy calculated over all the test samples): - Here, for each protein in the test set, we have three lines of information: - Line1: Identity of the protein (as '`name=.`'), length of the squence (as '`L=`'), and the recovery rate/accuracy for that protein sequence (as '`Recovery=`') - Line2: *Single-letter representation* of amino-acids of the ground truth sequences (as `true:`) - Line3: *Single-letter representation* of amino-acids of the predicted sequences by our method (as `pred:`) - An example file content: ``` >name=3fkf.A | L=141 | Recovery=0.5957446694374084 true:VTVGKSAPYFSLPNEKGEKLSRSAERFRNRYLLLNFWASWCDPQPEANAELKRLNKEYKKNKNFAMLGISLDIDREAWETAIKKDTLSWDQVCDFTGLSSETAKQYAILTLPTNILLSPTGKILARDIQGEALTGKLKELL pred:TAVGDEAPYFELPDLEGKKLSLDSEEFKNKYLLLDFWASWCLPCREEIAELKELYRRFAKNKKFAILGVSADTDKEAWLKAVKEDNLRWTQVSDFKGWDSEVFKNYNVQSLPENILLSPEGKILARGIRGEALRNKLKELL >name=2d9e.A | L=121 | Recovery=0.7685950398445129 true:GSSGSSGFLILLRKTLEQLQEKDTGNIFSEPVPLSEVPDYLDHIKKPMDFFTMKQNLEAYRYLNFDDFEEDFNLIVSNCLKYNAKDTIFYRAAVRLREQGGAVLRQARRQAEKMGSGPSSG pred:GSSGSSGRLTLLRETLEQLQERDTGWVFSEPVPLSEVPDYLDVIDHPMDFSTMRRKLEAHRYLSFDEFERDFNLIVENCRKYNAKDTVFYRAAVRLQAQGGAILRKARRDVESLGSGPSSG ```