File size: 1,635 Bytes
1555623 dd57259 1555623 38fca4d 1555623 dd57259 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
---
license: creativeml-openrail-m
base_model:
- Rostlab/prot_bert
---
[]
(https://colab.research.google.com/drive/1zccF8lGrF5rNQaSFPTd4wI-xvIJr-A78?usp=sharing)
ProtBERT-PI enables rapid screening of potential small secreted protease inhibitors in large-scale genomic, transcriptomic, or proteomic datasets.
The model assigns each input sequence to one of two classes:
Positive (Potential PI): Predicted to exhibit protease inhibitor activity
Negative (Non-PI): Predicted to lack protease inhibitor activity
Output includes:
Probability of the positive class (prob_class_1): ranges from 0 (low likelihood) to 1 (high likelihood of PI activity)
Confidence score: probability of the predicted class
Model Architecture and Training
ProtBERT-PI is a fine-tuned sequence classification model built on ProtBERT (BertForSequenceClassification):
Base model: Rostlab/prot_bert
Pre-trained on large corpora of protein sequences using masked language modeling
Fine-tuning was performed on a curated dataset of known protease inhibitors and non-protease inhibitor negative set.
Sequences are tokenized by inserting spaces between amino acids (standard for ProtBERT), enabling effective representation learning.
Maximum sequence length is configurable (default: 250 AA); longer sequences are truncated.
Positive examples: known protease inhibitors (<250 AA) from the MEROPS database
Negative examples: non-inhibitors selected from UniProt using sequence similarity and Pfam domain analysis
---
license: creativeml-openrail-m
--- |