--- title: "monoyield-predictor" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4e7fdf49-b603-4e8a-b71f-d58486dbb4a8" updated: "2026-11-13" --- # monoyield-predictor > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts how much of a protein sequence E. coli will make as a single-unit monomer, in log10 mg/L. Trained LoRA adapters and regression heads for ESMC-6B and SaProt, for protein researchers. monoyield-predictor is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Tsuboyama-Lab ## About ### What it is monoyield-predictor holds the trained weights of a model that predicts the monomeric expression yield of a protein sequence in E. coli, on a log10 mg/L scale. The files are LoRA adapters and regression heads, 7 to 12 MB per fold. The frozen base models are not included, since the code pulls them from Hugging Face at run time. ### Who it suits Protein researchers who want to estimate expression yield before running an experiment. The weights come with a bioRxiv preprint and a code repository. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Biotech | Protein engineers | Rank designed sequences by expected E. coli yield | Score a CSV of candidate sequences | | Academic labs | Protein researchers | Check a predictor against their own measurements | Compare predicted and measured yield | | Antibody work | Nanobody developers | Predict monomer yield or the monomer to dimer ratio for nanobodies | Score a set of nanobody sequences | | Research tools | Computational biologists | Use the adapters inside their own pipelines | Call the prediction script on new data | People without a large GPU, or who do not work with protein sequences, can skip it. ### Where to use it - A Python environment on Windows, macOS or Linux - A computer with a GPU of about 48 GB for fp32, or about 24 GB with bfloat16 - The companion code repository, which holds the scripts and example files | Use case | Who | What they get back | | --- | --- | --- | | De novo and mutation scans | Protein engineers | A predicted log10 monomer yield for each sequence | | Nanobody yield | Nanobody developers | A predicted log10 monomer yield for each nanobody | | Nanobody dimer ratio | Nanobody developers | A predicted log10 monomer to dimer ratio | ### How to set it up - Python and pip - A GPU with enough memory: about 48 GB for fp32, and about 24 GB for bfloat16, which the card says should be validated - The companion code repository, which the card names 1. Install the Hugging Face hub tools and download these weights if you want a local copy. 2. Get the companion code repository and move into its folder. 3. Install it in editable mode. 4. Run its download script to fetch the weights. ```sh pip install -U huggingface_hub hf download Tsuboyama-Lab/monoyield-predictor ``` ```bash cd monoyield-predictor pip install -e . HF_REPO=Tsuboyama-Lab/monoyield-predictor bash download_weights.sh weights ``` ### How to use it Run the prediction script on a CSV of sequences, with a weights folder and the base model it was trained on. Each folder holds five fold checkpoints, and a prediction is the mean of the five. The code repository lists all models, input formats and training commands. ```bash python scripts/predict_esmc_mlp_head.py --external_csv examples/example_main_library.csv \ --model_dir weights/esmc_monoyield --model_name biohub/ESMC-6B \ --model_revision 45b0fa5d7fb06faefbd5e3b89bdcef35d564e79a --dtype float32 --batch_size 1 --out_csv predictions.csv ``` | Tool | What it does | | --- | --- | | esmc\_monoyield | ESMC-6B adapter that reads an amino acid sequence and predicts log10 monomer yield | | saprot\_lora | SaProt adapter that reads a sequence plus Foldseek 3Di and predicts the same yield | | esmc\_nb\_monoyield | ESMC-6B adapter for nanobody sequences, predicting log10 monomer yield | | esmc\_nb\_mdratio | ESMC-6B adapter for nanobody sequences, predicting the log10 monomer to dimer ratio | ### Example You point the script at the example library file and the esmc\_monoyield folder. It scores each sequence with the ESMC-6B model and the five fold checkpoints, then writes the predictions to predictions.csv. ### For newcomers and experts This is research software for protein work, not a point and click app. A careful first step is reading the card's file table, then running the example CSV once you have a GPU with enough memory. Under the hood, each adapter adds low-rank matrices and a regression head to a frozen base model. Four variants cover de novo and mutation scan yield, a structure-aware SaProt version and two nanobody targets. Averaging five folds smooths out the variation between checkpoints. Sequences go in as a CSV file → The frozen base model reads each sequence → The adapter and head give a score per fold → The five folds are averaged → Predictions are written to a CSV file ### Good to know - Hardware: Full fp32 precision with ESMC-6B calls for a GPU holding roughly 48 GB. With bfloat16 it fits in 24 GB, though the card says to check its results first. - Base terms: The adapters are derivatives of ESMC-6B and SaProt, so you must follow those models' terms too. - ESMC-6B terms: an MIT license plus third-party notices, and the acceptable use policy from Biohub. The SaProt model uses the MIT license. - License: Apache 2.0 for the adapters. - Cost to run: none named. You need a GPU with enough memory, and no paid service appears in the source. - Data it reaches: Your sequence CSV, the base model files from Hugging Face and the companion code repository. - Not for: The source names no other forbidden uses. Follow the Biohub acceptable use policy that comes with ESMC-6B. ## README ````markdown # monoyield-predictor Predicts how much of a protein sequence E. coli will make as a single-unit monomer, in log10 mg/L. Trained LoRA adapters and regression heads for ESMC-6B and SaProt, for protein researchers. Made by Tsuboyama-Lab on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: monoyield-predictor holds the trained weights of a model that predicts the monomeric expression yield of a protein sequence in E. coli, on a log10 mg/L scale. The files are LoRA adapters and regression heads, 7 to 12 MB per fold. The frozen base models are not included, since the code pulls them from Hugging Face at run time. Who it suits: Protein researchers who want to estimate expression yield before running an experiment. The weights come with a bioRxiv preprint and a code repository. ```table Industry | Who uses it | How they use it | Example Biotech | Protein engineers | Rank designed sequences by expected E. coli yield | Score a CSV of candidate sequences Academic labs | Protein researchers | Check a predictor against their own measurements | Compare predicted and measured yield Antibody work | Nanobody developers | Predict monomer yield or the monomer to dimer ratio for nanobodies | Score a set of nanobody sequences Research tools | Computational biologists | Use the adapters inside their own pipelines | Call the prediction script on new data ``` People without a large GPU, or who do not work with protein sequences, can skip it. Where to use it: - A Python environment on Windows, macOS or Linux - A computer with a GPU of about 48 GB for fp32, or about 24 GB with bfloat16 - The companion code repository, which holds the scripts and example files ```table Use case | Who | What they get back De novo and mutation scans | Protein engineers | A predicted log10 monomer yield for each sequence Nanobody yield | Nanobody developers | A predicted log10 monomer yield for each nanobody Nanobody dimer ratio | Nanobody developers | A predicted log10 monomer to dimer ratio ``` How to set it up: - Python and pip - A GPU with enough memory: about 48 GB for fp32, and about 24 GB for bfloat16, which the card says should be validated - The companion code repository, which the card names 1. Install the Hugging Face hub tools and download these weights if you want a local copy. 2. Get the companion code repository and move into its folder. 3. Install it in editable mode. 4. Run its download script to fetch the weights. ```sh pip install -U huggingface_hub hf download Tsuboyama-Lab/monoyield-predictor ``` ```bash cd monoyield-predictor pip install -e . HF_REPO=Tsuboyama-Lab/monoyield-predictor bash download_weights.sh weights ``` How to use it: Run the prediction script on a CSV of sequences, with a weights folder and the base model it was trained on. Each folder holds five fold checkpoints, and a prediction is the mean of the five. The code repository lists all models, input formats and training commands. ```bash python scripts/predict_esmc_mlp_head.py --external_csv examples/example_main_library.csv \ --model_dir weights/esmc_monoyield --model_name biohub/ESMC-6B \ --model_revision 45b0fa5d7fb06faefbd5e3b89bdcef35d564e79a --dtype float32 --batch_size 1 --out_csv predictions.csv ``` ```table Tool | What it does esmc_monoyield | ESMC-6B adapter that reads an amino acid sequence and predicts log10 monomer yield saprot_lora | SaProt adapter that reads a sequence plus Foldseek 3Di and predicts the same yield esmc_nb_monoyield | ESMC-6B adapter for nanobody sequences, predicting log10 monomer yield esmc_nb_mdratio | ESMC-6B adapter for nanobody sequences, predicting the log10 monomer to dimer ratio ``` Example: You point the script at the example library file and the esmc_monoyield folder. It scores each sequence with the ESMC-6B model and the five fold checkpoints, then writes the predictions to predictions.csv. For newcomers and experts: This is research software for protein work, not a point and click app. A careful first step is reading the card's file table, then running the example CSV once you have a GPU with enough memory. Under the hood, each adapter adds low-rank matrices and a regression head to a frozen base model. Four variants cover de novo and mutation scan yield, a structure-aware SaProt version and two nanobody targets. Averaging five folds smooths out the variation between checkpoints. ```flow Sequences go in as a CSV file The frozen base model reads each sequence The adapter and head give a score per fold The five folds are averaged Predictions are written to a CSV file ``` Good to know: - Hardware: Full fp32 precision with ESMC-6B calls for a GPU holding roughly 48 GB. With bfloat16 it fits in 24 GB, though the card says to check its results first. - Base terms: The adapters are derivatives of ESMC-6B and SaProt, so you must follow those models' terms too. - ESMC-6B terms: an MIT license plus third-party notices, and the acceptable use policy from Biohub. The SaProt model uses the MIT license. - License: Apache 2.0 for the adapters. - Cost to run: none named. You need a GPU with enough memory, and no paid service appears in the source. - Data it reaches: Your sequence CSV, the base model files from Hugging Face and the companion code repository. - Not for: The source names no other forbidden uses. Follow the Biohub acceptable use policy that comes with ESMC-6B. ## On Hugging Face, 13 November 2026 - Library: peft - Downloads in the last 30 days: 0 - Likes: 3 - License: apache-2.0 - Based on: biohub/ESMC-6B - Tags: peft, protein, protein-language-model, lora, expression-yield, nanobody ## Links - Model: https://huggingface.co/Tsuboyama-Lab/monoyield-predictor - Files: https://huggingface.co/Tsuboyama-Lab/monoyield-predictor/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Tsuboyama-Lab/monoyield-predictor ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Tsuboyama-Lab/monoyield-predictor on Hugging Face](https://huggingface.co/Tsuboyama-Lab/monoyield-predictor) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | monoyield-predictor (this page) | 0 | 3 | — | apache-2.0 | 2026-11-05 | | [OpenDDE](https://mindket.com/p/6617ce0b-24d9-4350-804f-4ca6808bae9d) | 10,009 | 12 | — | apache-2.0 | 2026-08-14 | | [Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-10-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Tsuboyama-Lab/monoyield-predictor on Hugging Face](https://huggingface.co/Tsuboyama-Lab/monoyield-predictor) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4e7fdf49-b603-4e8a-b71f-d58486dbb4a8) - [Its page on Mindket](https://mindket.com/p/4e7fdf49-b603-4e8a-b71f-d58486dbb4a8)