BERT-Based Antimicrobial Peptide Function Prediction
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Solution Overview
Problem
Current artificial intelligence methods face limitations in predicting the function and sequence characteristics of antimicrobial peptides, which hinders the identification of effective antimicrobial peptides against antibiotic-resistant bacteria.
Innovation Solution
An apparatus and method using artificial intelligence, specifically a pre-trained natural language processing model like BERT, to tokenize amino acid sequences, generate sequence embedding vectors, and determine antimicrobial peptide function through self-attention and multi-head attention mechanisms, identifying crucial amino acids contributing to the peptide's function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AI methods are used to predict antimicrobial peptide function, then prediction can be performed, but prediction accuracy is insufficient and sequence characteristics cannot be properly identified
Solution Approach 1:
The patent transforms the peptide sequence representation by tokenizing amino acids and adding position information encoding, changing the input parameters from raw sequences to structured token representations with positional context. This parameter transformation enables the model to capture sequence characteristics more effectively and improve prediction accuracy while reliably identifying functional regions.
Solution Approach 2:
The patent adds a positional dimension to the peptide sequence analysis by encoding position information for each token. This dimensional enhancement allows the model to understand not only which amino acids are present but also their sequential arrangement and relative positions, thereby improving both prediction accuracy and the ability to identify sequence characteristics critical for antimicrobial function.
2Reliability
If laboratory screening methods are used to identify antimicrobial peptides, then effective peptides can be found, but time and cost are excessively high
Solution Approach 1:
The patent creates a computational copy of the laboratory screening process through an AI model that mimics the biological activity prediction. Instead of physically testing each peptide in the lab, the system uses a trained neural network model to predict antimicrobial function and identify sequence characteristics, dramatically reducing time and cost while maintaining reliable identification capability.
Solution Approach 2:
The patent replaces the mechanical laboratory screening system with an information-processing AI system. The computational model processes peptide sequences through tokenization, embedding, and attention mechanisms to predict function and identify characteristics, substituting physical wet-lab experiments with virtual computational analysis that is faster and more cost-effective while preserving identification reliability.
3Speed
If simple AI models are used for prediction, then computation is fast, but the model cannot identify which amino acids are important for function
Solution Approach 1:
The patent introduces attention mechanisms as an intermediary layer between the sequence embedding and the final prediction. This intermediary computes attention scores that measure the importance of each amino acid position, allowing the model to maintain fast computation through efficient matrix operations while simultaneously identifying which amino acids contribute most to antimicrobial function through the attention weights.
Data Source
AI summary
Provided are an apparatus and method for determining antimicrobial peptide function using artificial intelligence. The artificial intelligence, which is a bidirectional encoder representation from transformer (BERT)-based model that is pre-trained through unsupervised learning using large amounts of protein sequences may be additionally trained (fine-tuned) using labeled antimicrobial peptide and non-antimicrobial peptide sequences to improve accuracy of determining the peptide function.