文献类型：期刊文献

英文题名：Computational Design of Potentially Multifunctional Antimicrobial Peptide Candidates via a Hybrid Generative Model

作者：Ying, Fangli[1];Go, Wilten[1];Li, Zilong[1];Ouyang, Chaoqian[1];Phaphuangwittayakul, Aniwat[2];Dhuny, Riyad[3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]Chiang Mai Univ, Int Coll Digital Innovat, Chiang Mai 50200, Thailand;[3]Univ Technol, Dept Creat Arts Film & Media Technol, Pointe Aux Sables 11134, Mauritius

年份：2025

卷号：26

期号：15

外文期刊名：INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001552671500001)】；

基金：This research was funded by National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China, NO. 32327801; This research was partially funded by National Key Research and Development Program of China, No2020YFA0907800; This research was partially funded by Research and Development Plan in Shandong Province No.2022CXGC020206; This research was partially funded by the Key R & D Program of Shandong Province, China, grant number 2022SFGC0104.

语种：英文

外文关键词：antimicrobial peptides; GANs; Deep Generative Models

摘要：Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network's ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections.