https://github.com/lamm-mit/peptideminer
https://github.com/lamm-mit/peptideminer
Last synced: over 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/lamm-mit/peptideminer
- Owner: lamm-mit
- License: apache-2.0
- Created: 2024-11-07T09:00:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-09T06:24:41.000Z (over 1 year ago)
- Last Synced: 2024-12-09T07:25:36.749Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.37 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PeptideMiner: Learning the rules of peptide self-assembly through data mining with large language models
Zhenze Yang [1,2], Sarah K. Yorke [3], Tuomas P. J. Knowles [3], and Markus J. Buehler [1,4,5]
[1] Laboratory for Atomistic and Molecular Mechanics, Department of Civil and Environmental Engineering,
Massachusetts Institute of Technology, 77 Massachusetts Ave. Room 1-165, Cambridge, 02139, MA, USA
[2] Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave.,
Cambridge, 02139, MA, USA
[3] Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
[4] Center for Computational Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge,
02139, MA, USA
[5] Center for Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave.,
Cambridge, 02139, MA, USA
Corresponding authors: mbuehler@mit.edu, tpjk2@cam.ac.uk
ABSTRACT: Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the impacts of both internal chemical composition and external environmenta stimuli on the self-assembly behaviour of these systems. However, there is yet to be a systematic study that gathers this rich literature data and collectively examines these experimental factors to provide a global picture of the fundamental rules that govern protein self-assembly behavior. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining facilitated by a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the collected data, ML models are trained and evaluated, demonstrating excellent accuracy (>80%) and efficiency in peptide assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. We find that this workflow can substantially improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. In doing so, novel structures can be accessed for a range of applications including sensing, catalysis and biomaterials.
Overview of the workflow reported in this work}. We first collect PDF files of publications from different journal presses and scientific databases based on the previous polypeptide database SAPdb \cite{MATHUR2021104391}. Here, we extract not only the peptide sequence but also experimental conditions from those previous publications and learn their impacts on the self-assembly phase of polypeptides. The selected publications are read and processed by human experts to curate the database, which is further utilized to train ML algorithms for predicting self-assembled structure from peptide sequences and experimental conditions. We also use the manually curated database to fine-tune a LLM to be specialized in literature mining of polypeptide publications and compare the performance with the same LLM without fine-tuning. The model can be used to extract information for new publications, significantly reducing the time required compared to manual methods employed by human experts. Moreover, by incorporating this new data, we can augment our existing database, thereby further refining and enhancing our ML model for phase prediction.
## Installation
```
git clone https://github.com/lamm-mit/PeptideMiner.git
cd PeptideMiner
conda create -n [your_env]
conda activate [your_env]
pip install -r requirements.txt
```
## Usage
### Data/
- The **train.jsonl** and **test.jsonl** store the processed text data used for training and testing purposes for comparison between the original and fine-tuned GPT models.
- The **phase_data_clean.csv** stores the clean data of experimental conditions and corresponding self-assembly phases of different peptides based on text mining.
### scripts/
- The **comparison/** folder includes notbooks for the inference process and training snippet for the original and fine-tuned GPT models. All fine-tuning were done using OpenAI API platform.
```
pretrain.ipynb
fine-tuning.ipynb
```
- The **phase_predictor/** folder includes notebooks for the whole workflow of building classical ML models for phase prediction given peptide sequence and other experimental conditins.
```
train_and_inference.ipynb
```
## Reference
```bibtex
@article{YangYorkeKnowlesBuehler2024,
title={PeptideMiner: Learning the rules of peptide self-assembly through data mining with large language models},
author={Zhenze Yang and Sarah K. Yorke and Tuomas P. J. Knowles and Markus J. Buehler},
year={2024},
eprint={},
archivePrefix={arXiv},
primaryClass={},
url={https://arxiv.org/abs/XXX.YYYYYY},
}
```
- We used OpenAPI API for text mining and fine-tuning with "GPT-3.5-turbo-0215" model.
```bibtex
@misc{OpenAIGPT3.5Turbo,
title = {GPT-3.5-turbo-0125},
author = {{OpenAI}},
year = {2023},
howpublished = {\url{https://openai.com/api/}},
note = {Accessed: 2023-04-07}
}
```