Human Proteome Mining for Encrypted Antimicrobial Peptides
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Solution Overview
Problem
The rise of antibiotic-resistant infections poses a significant threat due to the lack of innovation in antibiotic discovery, with existing antibiotics often having side effects and selecting for resistance, necessitating the development of new antimicrobial agents.
Innovation Solution
Computational methods are used to identify candidate encrypted peptides with predicted antimicrobial activity by scanning the human proteome for peptides with specific physicochemical features, such as length, charge, and hydrophobicity, and synthesizing and testing these peptides for their antimicrobial properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional antibiotics are used to treat infections, then bacterial infections can be controlled, but antibiotic resistance develops and side effects occur
Solution Approach 1:
The patent changes the fundamental parameters of antimicrobial agents by transitioning from conventional small-molecule antibiotics to peptide-based antimicrobials derived from the human proteome. These peptides exhibit different physicochemical properties (amphipathic structure, cationic charge) that enable them to kill bacteria through membrane disruption rather than traditional targets, thereby overcoming resistance while maintaining effectiveness
Solution Approach 2:
The patent identifies and utilizes peptides that are naturally encoded within human protein sequences (encrypted peptides). By computationally mining the human proteome for hidden peptide sequences and synthesizing them, the invention copies nature's own defense mechanisms that already exist within the human body, providing antimicrobial activity without introducing foreign substances that trigger resistance
2Productivity
If the human proteome is scanned to identify encrypted peptides, then novel antimicrobial agents can be discovered, but computational and experimental resources are required
Solution Approach 1:
The patent performs preliminary computational screening of the entire human proteome to identify candidate encrypted peptides before experimental validation. By using in silico methods to predict peptide properties (amphipathic potential, charge, hydrophobicity) and filter sequences accordingly, the invention narrows down millions of possible sequences to a manageable subset of high-probability candidates for synthesis and testing
Solution Approach 2:
The patent divides the complex task of antimicrobial discovery into separate stages: (1) computational identification of candidate peptides from protein sequences, (2) in silico filtering based on physicochemical properties, (3) synthesis of selected candidates, and (4) experimental validation of antimicrobial activity. This segmentation allows each step to be optimized independently and reduces overall complexity
Data Source
AI summary
Disclosed are methods for computationally identifying candidate antimicrobial peptides by scanning the human protcome, ranking identified sequences according to a scoring system in order to identify the candidate sequences. Also provided are novel candidate antimicrobial peptides, as well as combinations of peptides that exhibit synergistic antimicrobial properties.