biodatalearning / udsm-llps-syn Goto Github PK

In this study, we applied the deep sequence model – UDSMProt to two new protein classification tasks.

1. predict proteins with liquid-liquid phase separation propensity
2. predict synaptic proteins
   

Our results have shown that, without prior domain knowledge and only based on protein sequences, the fine-tuned language models achieved high classification accuracies and outperformed baseline models using compositional k-mer features in both tasks. For details of this work, please refer to our paper "Deep sequence representation learning for predicting human proteins with liquid-liquid phase separation propensity and synaptic functions" (Wei and Wang, 2022)

Please refer to the orignal repository of UDSMProt for detailed information.

Users are welcome to use the fine-tuned models in both learning tasks for comparisons in their own research.
Here, we provide one example to show the application of the fine-tuned UDSM-LLPS models in the first learning task. As stated in our paper, in addition to LLPSDB and PhaSepDB data, we also evaluated the performance of UDSM-LLPS on another well-known database – DrLLPS. DrLLPS is currently the most comprehensive database with the largest collection of LLPS-associated proteins in 164 eukaryotes. In DrLLPS, LLPS-associated proteins can be browsed by three LLPS types, including

• scaffolds, proteins that can drive or undergo LLPS;
• clients, proteins that can be recruited by scaffolds for the formation of biomolecular condensates;
• regulators, proteins that have not been identified to undergo LLPS but shown to be involved in regulating LLPS behaviors.

• DrLLPS data: task_1/application/DrLLPS_data.csv stores 3627 reviewed human LLPS-associated proteins categorized by the three types, consisting of 100 scaffolds, 2,998 clients, and 529 regulators.
• Fine-tuned UDSM-LLPS models: UDSM-LLPS_Random.pkl and UDSM-LLPS_UniRef.pkl under task_1/
• Utils file: model_utils.py downloaded from the original UDSMProt repository
• Token file: tok_itos.npy

Please see two Jupyter Notebooks under task_1/application/ for detailed steps:

• 1. Predict LLPS propensity of DrLLPS data.ipynb
• 2. UDSM-LLPS prediction results on DrLLPS data.ipynb