Development of a Machine Learning-Based  Predictive Model for Helical Antimicrobial  Peptides Against Specific Strains

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蔡政庭(Cheng-Ting Tsai) 查詢紙本館藏

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化學學系

論文名稱

(Development of a Machine Learning-Based Predictive Model for Helical Antimicrobial Peptides Against Specific Strains)

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至系統瀏覽論文 (2024-7-15以後開放)

摘要(中)

過度使用抗生素已導致多重藥物耐藥（MDR）病原體的出現，對公共衛生造成了重大
威脅。從結構和化學上多樣的化合物中衍生出的抗微生物肽（AMPs）為解決這個問題提
供了一個有前途的解決方案。然而，由於流體膜中結構-功能關係的動態性質，鑒定有效
的 AMPs 一直具有挑戰性。機器學習（ML）算法已被應用來應對這一挑戰，我們使用 ML
基於螺旋狀AMP 序列訓練了一個預測模型，以針對特定的細菌進行靶向。已開發了多種
ML 模型來預測 AMPs。然而，這些方法大多主要依賴於氨基酸序列，對於預測針對特定
菌株的抗微生物活性，僅有限地納入了 3D 結構特徵和 ML 模型內的特定相互作用。在本
研究中，我們提出了一個線性 AMP 的預測模型，該模型包括以下重要特徵：（i）透過圖
論將AMP 的 3D 結構進行比較；（ii）根據分子動力學模擬推導出的特定相互作用的實現；
（iii）考慮到細菌的負電性特性的 AMP 膜插入模型。此外，我們的預測模型已經開發
出來區分對特定菌株具有活性的肽與其他肽，從而增強了 AMP 開發中的特異性。此外，
我們還使用了低序列識別的肽來評估我們模型的準確性。我們的預測模型可以快速設計
出有效的 AMPs。設計了 7 個潛在的 AMPs 並進行了體外細胞實驗，顯示成功率高達 71
％，以提高 AMPs 的抗菌效果。 AMP-META 可免費訪問 http://ai-
meta.chem.ncu.edu.tw/amp-meta

摘要(英)

Excessive use of antibiotics has led to the emergence of multidrug-resistant (MDR)
pathogens, which pose a significant threat to public health. Antimicrobial peptides (AMPs)
derived from structurally and chemically diverse compounds offer a promising solution to
combat this problem. However, identifying effective AMPs has been challenging due to the
dynamic nature of structure-function relationships in fluid membranes. Machine learning (ML)
algorithms have been employed to address this challenge, and we trained a predictive model
based on helical AMP sequences using ML to target specific bacteria. Various ML models have
been developed to predict AMPs. However, most of these methods have primarily relied on
amino acid sequences, with limited incorporation of 3D structural features and specific
interactions within ML models to predict antimicrobial potency against specific strains. In this
study, we present a predictive model for linear AMPs, which incorporates the following
significant features: (i) Graph theory considerations, taking into account the native contacts of
3D AMPs; (ii) Implementation of specific interactions derived from molecular dynamics
simulations; and (iii) Incorporation of a membrane-insertion model of AMPs that considers the
negatively-charged nature of bacteria. Furthermore, our predictive model has been developed
to differentiate peptides that are active against particular strains from others, thereby enhancing
the specificity in AMP development. Additionally, low-sequence-identified peptides were
employed to evaluate the accuracy of our model. Our predictive models enable rapid
engineering of potent AMPs. Designed 7 potential AMPs and conducted in vitro cell
experiments, showing a success rate of up to 71% to improve the antibacterial effect of AMPs.
AMP-META is freely accessible at http://ai-meta.chem.ncu.edu.tw/amp-meta

關鍵字(中)

★ 機器學習
★ 抗菌肽
★ 三維結構
★ 預測模型
★ LightGBM
★ AMP-META

關鍵字(英)

★ Machine Learning
★ Antimicrobial peptides
★ 3D structure
★ Predictive model
★ LightGBM
★ AMP-META

論文目次

Contents
摘要 ............................................................................................................................................. i
Abstract ....................................................................................................................................... ii
Acknowledgment ....................................................................................................................... iii
Contents .................................................................................................................................... iv
List of Figures ............................................................................................................................ vi
List of Tables ........................................................................................................................... viii
Chapter 1— Introduction ........................................................................................................ 1
The Origins, Overuse, and Challenges of Antibiotics: Mechanisms and
Development Difficulties. .................................................................................................. 1
Understanding the Mechanisms of Action of Antimicrobial Peptides: A
Fundamental Overview. ...................................................................................................... 3
A Review of Current AI Literature in AMPs .......................................................... 5
In this study ............................................................................................................ 8
Chapter 2— Methods ............................................................................................................ 11
2.1 Database................................................................................................................ 11
2.2 Construction Training Dataset .............................................................................. 13
2.3 Construction Validation set, Test set and New AMP set ....................................... 19
2.4 Feature .................................................................................................................. 21
2.4.1 Sequence ................................................................................................... 21
2.4.2 Physicochemical Properties ...................................................................... 23
2.4.3 Position-specific Features ......................................................................... 30
2.4.4 AMPs interaction with membrane ............................................................ 35
2.5 Algorithm ............................................................................................................. 36
2.5.1 Light Gradient Boosting Machine ............................................................ 36
2.5.2 Support Vector Machine ........................................................................... 40
2.5.3 Artificial Neural Network ......................................................................... 42
2.5.4 Convolutional Neural Network ................................................................. 44
2.5.5 Recurrent Neural Network ........................................................................ 46
2.5.6 Activation function .................................................................................... 47
2.6 Performance evaluation ........................................................................................ 49
Chapter 3— Result and Discussion ....................................................................................... 53
3.1 Performance of Predictive Models ....................................................................... 56
3.1.1 Performance of Predictive Models against E. Coli ATCC 25922 ............ 56
3.1.2 Performance of Predictive Models against P. aeruginosa ATCC 27853 .. 61
3.1.3 Performance of Predictive Models against S. aureus ATCC 25923 ......... 67
3.1.4 Performance of Predictive Models against Human Erythrocyte .............. 71

v

3.1.5 In Silico Testing of Predictive Models ..................................................... 77
3.1.6 Summary ................................................................................................... 80
3.2 In Silico Design of AMPs and In Vitro MIC Test ................................................ 81
Chapter 4— Conclusion ........................................................................................................ 85
Reference .................................................................................................................................. 88
Supporting Information ............................................................................................................ 97

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指導教授

蔡惠旭(Hui-Hsu Gavin Tsai)

審核日期

2023-7-13

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