A foundation model approach to guide antimicrobial peptide design in the era of artificial intelligence driven scientific discovery

• Published in: arXiv.org
• Main Authors: Wang, Jike, Feng, Jianwen, Kang, Yu, Pan, Peichen, Ge, Jingxuan, Wang, Yan, Wang, Mingyang, Wu, Zhenxing, Zhang, Xingcai, Yu, Jiameng, Zhang, Xujun, Wang, Tianyue, Wen, Lirong, Guangning Yan, Deng, Yafeng, Shi, Hui, Chang-Yu, Hsieh, Jiang, Zhihui, Hou, Tingjun
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 17.07.2024
• Subjects: Antibiotics; Antiinfectives and antibacterials; Artificial intelligence; Bacteria; Blood plasma; Candidates; Design; Drug resistance; Extreme values; Load resistance; Machine learning; Peptides; Public health
• ISSN: 2331-8422

We propose AMP-Designer, an LLM-based foundation model approach for the rapid design of novel antimicrobial peptides (AMPs) with multiple desired properties. Within 11 days, AMP-Designer enables de novo design of 18 novel candidates with broad-spectrum potency against Gram-negative bacteria. Subsequent in vitro validation experiments demonstrate that almost all in silico recommended candidates exhibit notable antibacterial activity, yielding a 94.4% positive rate. Two of these candidates exhibit exceptional activity, minimal hemotoxicity, substantial stability in human plasma, and a low propensity of inducing antibiotic resistance as observed in murine lung infection experiments, showcasing their significant efficacy in reducing bacterial load by approximately one hundredfold. The entire process, from in silico design to in vitro and in vivo validation, is completed within a timeframe of 48 days. Moreover, AMP-Designer demonstrates its remarkable capability in designing specific AMPs to target strains with extremely limited labeled datasets. The most outstanding candidate against Propionibacterium acnes suggested by AMP-Designer exhibits an in vitro minimum inhibitory concentration value of 2.0 \(\mu\)g/ml. Through the integration of advanced machine learning methodologies such as contrastive prompt tuning, knowledge distillation, and reinforcement learning within the AMP-Designer framework, the process of designing AMPs demonstrates exceptional efficiency. This efficiency remains conspicuous even in the face of challenges posed by constraints arising from a scarcity of labeled data. These findings highlight the tremendous potential of AMP-Designer as a promising approach in combating the global health threat of antibiotic resistance.