Abstract:Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based pep… Show more
“…Deep learning techniques have been widely applied in biological related fields. Recently, we have developed LSTM_pep for de novo potential bioactive peptide generation through finetune training over known active peptides, and DeepPep for prediction of whether given protein-peptide are binding 9 . The combination of LSTM_Pep and DeepPep can provide an effective way to generate and screen potential active peptides for given protein targets.…”
In our previous work, we have developed LSTM_Pep to generate de novo potential active peptides by finetuning with known active peptides and developed DeepPep to effectively identify protein-peptide interaction. Here, we have combined LSTM_Pep and DeepPep to successfully obtained an active de novo peptide (ARG-ALA-PRO-GLU) of Xanthine oxidase (XOD) with IC50 value of 3.76mg/mL, and XOD inhibitory activity of 64.32%. Consistent with the experiment result, the peptide ARG-ALA-PRO-GLU has the highest DeepPep score, this strongly supports that we can generate de novo potential active peptides by finetune training LSTM_Pep over some known active peptides and identify those active peptides by DeepPep effectively. Our work sheds light on the development of deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.
“…Deep learning techniques have been widely applied in biological related fields. Recently, we have developed LSTM_pep for de novo potential bioactive peptide generation through finetune training over known active peptides, and DeepPep for prediction of whether given protein-peptide are binding 9 . The combination of LSTM_Pep and DeepPep can provide an effective way to generate and screen potential active peptides for given protein targets.…”
In our previous work, we have developed LSTM_Pep to generate de novo potential active peptides by finetuning with known active peptides and developed DeepPep to effectively identify protein-peptide interaction. Here, we have combined LSTM_Pep and DeepPep to successfully obtained an active de novo peptide (ARG-ALA-PRO-GLU) of Xanthine oxidase (XOD) with IC50 value of 3.76mg/mL, and XOD inhibitory activity of 64.32%. Consistent with the experiment result, the peptide ARG-ALA-PRO-GLU has the highest DeepPep score, this strongly supports that we can generate de novo potential active peptides by finetune training LSTM_Pep over some known active peptides and identify those active peptides by DeepPep effectively. Our work sheds light on the development of deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.
“…Simultaneously, these enhancements have amplified both the capabilities and dualuse risks (potential risks associated with technology that can be used for both beneficial or malicious purposes) of biological design tools (BDTs) (5). BDT's are increasingly able to assist in protein engineering and design (6)(7)(8)(9). Consequently, whether inadvertently or with malicious intent, BDT's may create dangerous biomolecules.…”
In-silico toxin classification assists in industry and academic endeavors and is critical for biosecurity. For instance, proteins and peptides hold promise as therapeutics for a myriad of conditions, and screening these biomolecules for toxicity is a necessary component of synthesis. Additionally, with the expanding scope of biological design tools, improved toxin classification is essential for mitigating dual-use risks. Here, a general toxin classifier that is capable of addressing these demands is developed. Applications forin-silicotoxin classification are discussed, conventional and contemporary methods are reviewed, and criteria defining current needs for general toxin classification are introduced. As contemporary methods and their datasets only partially satisfy these criteria, a comprehensive approach to toxin classification is proposed that consists of training and validating a single sequence classifier, BioLMTox, on an improved dataset that unifies current datasets to align with the criteria. The resulting benchmark dataset eliminates ambiguously labeled sequences and allows for direct comparison against nine previous methods. Using this comprehensive dataset, a simple fine-tuning approach with ESM-2 was employed to train BioLMTox, resulting in accuracy and recall validation metrics of 0.964 and 0.984, respectively. This LLM-based model does not use traditional alignment methods and is capable of identifying toxins of various sequence lengths from multiple domains of life in sub-second time frames.
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