Fine-Tuned Protein Language Models for Antimicrobial Peptide Design
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Solution Overview
Problem
Current methods for discovering antimicrobial peptides (AMPs) are inefficient and resource-intensive, overlooking rare or elusive AMPs and hindering rapid identification, while AI-based approaches struggle with limited data and generalization, leading to sub-optimal results.
Innovation Solution
A deep learning-based framework using pretrained protein large language models with fine-tuning techniques, combined with machine learning and bioinformatics tools, for designing antimicrobial peptides, including supervised training, adapter-based steps, and comprehensive filtering processes to enhance peptide generation and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual isolation and purification methods are used to discover AMPs, then structural and functional characteristics can be identified, but the process is resource-intensive and time-consuming
Solution Approach 1:
The patent uses AI models to create virtual copies of AMP discovery processes, replacing physical isolation and purification steps with computational predictions. The models generate predicted AMP sequences and structures without requiring actual sample collection and laboratory analysis, dramatically reducing time while maintaining identification accuracy through trained predictive algorithms
Solution Approach 2:
The patent performs preliminary computational screening and prediction of AMP candidates before actual experimental validation. By pre-filtering and pre-characterizing potential AMPs using AI models, the system reduces the number of samples requiring manual isolation and purification, thereby reducing overall discovery time while maintaining accurate structural and functional identification
2Reliability
If traditional manual curation methods are used, then AMP candidates can be screened, but the exploration of vast AMP landscape is impeded
Solution Approach 1:
The patent replaces manual curation and screening processes with AI-based computational systems. The models automatically analyze vast datasets of AMP sequences and structures, performing screening at scale without human intervention. This substitution maintains reliable identification of AMP characteristics while dramatically improving productivity by exploring the vast AMP landscape that would be impossible to analyze manually
3Ease of manufacture
If deep learning models are trained on limited AMP datasets, then model training is feasible, but generalization performance deteriorates
Solution Approach 1:
The patent trains AI models on multi-source datasets including not only experimentally validated AMPs but also related protein sequences and structural data from public databases. This multi-functional training approach allows the model to learn universal patterns across diverse biological sequences, improving generalization performance while maintaining training feasibility by leveraging abundant publicly available data beyond limited AMP-specific datasets
Solution Approach 2:
The patent performs preliminary data augmentation and preprocessing to expand limited AMP datasets before model training. By generating synthetic AMP variants, adding related sequence data, and pre-processing public database entries, the system creates enriched training datasets that improve model generalization while keeping the actual training process computationally feasible
Data Source
AI summary
A method for functional antimicrobial peptide design is provided. The method includes running pretrained protein large language models as a generator and enhancing a sample candidate. The enhancing a sample candidate includes performing an automatic pipeline based on machine learning methods and a plurality of bioinformatics methods and is configured to perform protein inverse folding and computational protein sequence designing. The computational protein sequence designing includes running a pretrained deep learning-based protein structure model, an autoregressive pretrained protein sequence model, and a deep learning-based alignment model. The pretrained deep learning-based protein structure model is configured to learn three-dimensional structures of the proteins and output latent structure embeddings in a high dimensional space. The autoregressive pretrained protein sequence model includes a protein language model to automatically generate protein sequences and is trained to predict next amino acid in a protein sequence based on a preceding sequence(s) of amino acids.