{"title":"TriStack enables accurate identification of antimicrobial and anti-inflammatory peptides by combining machine learning and deep learning approaches","authors":"","doi":"10.1016/j.future.2024.07.024","DOIUrl":null,"url":null,"abstract":"<div><p>The identification of antimicrobial peptides (AMPs) and anti-inflammatory peptides (AIPs) is crucial for drug design and disease treatment. However, it remains a computational challenge to accurately identify these peptides due to insufficient information encoding the peptide sequences. In this study, we propose TriStack, a powerful and interpretable model for accurate identification of AMPs and AIPs by stacking a machine learning-based module using a multi-layer residual network. It first extracts three types of function-related features from peptide sequences to comprehensively characterize the composition, distribution, and physicochemical properties of residues. Furthermore, these features are fused and fed into a two-module stacked model. The first module provides the preliminary predictions based on three machine learning methods, while the second module refines these predictions further via a multi-layer residual network. After training and testing, TriStack outperforms all the compared leading methods for both AMPs and AIPs predictions. TriStack is expected to contribute to antimicrobial and anti-inflammatory drug based on peptide sequences.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X2400390X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The identification of antimicrobial peptides (AMPs) and anti-inflammatory peptides (AIPs) is crucial for drug design and disease treatment. However, it remains a computational challenge to accurately identify these peptides due to insufficient information encoding the peptide sequences. In this study, we propose TriStack, a powerful and interpretable model for accurate identification of AMPs and AIPs by stacking a machine learning-based module using a multi-layer residual network. It first extracts three types of function-related features from peptide sequences to comprehensively characterize the composition, distribution, and physicochemical properties of residues. Furthermore, these features are fused and fed into a two-module stacked model. The first module provides the preliminary predictions based on three machine learning methods, while the second module refines these predictions further via a multi-layer residual network. After training and testing, TriStack outperforms all the compared leading methods for both AMPs and AIPs predictions. TriStack is expected to contribute to antimicrobial and anti-inflammatory drug based on peptide sequences.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.