Peptide Design Model for Unnatural Amino Acid Binding Specificity
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Solution Overview
Problem
Existing peptide design schemes struggle to accurately incorporate unnatural amino acids due to their complex structural compatibility, leading to issues such as poor membrane permeability and low metabolic stability, which hinder the development of effective peptide drugs.
Innovation Solution
A model-based peptide design method that utilizes a pre-trained peptide design model to predict and design unnatural amino acids at reserved positions in peptides, leveraging multimodal features like backbone orientation, backbone rotation, side chain type, and rigid atom group distribution, and performs validity checks to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing peptide design schemes are used to incorporate unnatural amino acids, then the structural diversity of peptides can be enhanced, but the structural compatibility and binding specificity deteriorate due to the complex nature of unnatural amino acids
Solution Approach 1:
The invention transforms the peptide design problem into a parameter optimization problem by using a pre-trained model that predicts optimal parameters for unnatural amino acid incorporation. The model takes as input the target protein pocket features and existing peptide sequence, then outputs predicted parameters including the type of unnatural amino acid, its position, and structural conformation parameters, thereby achieving precise control over binding specificity while maintaining structural diversity
Solution Approach 2:
The invention uses a pre-trained peptide design model that has been trained on extensive peptide-protein binding data to 'copy' successful design patterns and structural configurations. The model learns from training examples and applies this knowledge to predict optimal unnatural amino acid designs for new target peptides, effectively copying proven design strategies without requiring de novo structural analysis
2Adaptability or versatility
If unnatural amino acids are incorporated into peptide chains, then functional diversity and pharmacological properties are improved, but membrane permeability and metabolic stability deteriorate
Solution Approach 1:
The model simultaneously optimizes multiple parameters including not only the type and position of unnatural amino acids but also their structural conformations and physicochemical properties. By adjusting these parameters within the predicted optimal range, the design achieves enhanced functional diversity while maintaining metabolic stability and membrane permeability through balanced parameter selection
3Ease of operation
If manual design methods are used for peptide optimization, then the design process can be flexible and adaptive, but the accuracy and efficiency of incorporating unnatural amino acids deteriorate
Solution Approach 1:
The system employs a pre-trained model that has copied and stored extensive knowledge from training data about successful peptide designs. This allows the system to automatically apply proven design patterns with high accuracy while maintaining ease of operation through automated prediction, eliminating the need for manual trial-and-error optimization
Solution Approach 2:
The pre-trained peptide design model serves as an intermediary between the target protein structure and the final peptide design. It processes the input features (pocket structure, existing peptide sequence) and translates them into optimized design parameters, acting as a mediator that bridges structural analysis and design optimization while ensuring both accuracy and efficiency
Data Source
AI summary
A model-based peptide design method in the field of artificial intelligence technology such as biological computing is provided. The specific implementation includes: obtaining a pocket of an objective target protein and an objective peptide, a reserved position for designing a unnatural amino acid is identified in the objective peptide, and the pocket binds to the objective peptide via the reserved position; obtaining a feature of the pocket of the objective target protein and multimodal features of each known amino acid in the objective peptide; the multimodal features of each known amino acid comprise a backbone orientation feature, a backbone rotation feature, a side chain type feature, and a rigid atom group distribution feature; designing the unnatural amino acid at the reserved position in the objective peptide using a pre-trained peptide design model based on the feature of the pocket of the objective target protein and the multimodal features of each known amino acid in the objective peptide.