Xie Chengzhi, Wei Yijie, Luo Xinwei, Yang Huan, Lai Hongyan, Dao Fuying, Feng Juan, Lv Hao
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Glasgow College, University of Electronic Science and Technology of China, Chengdu, 610054, China.
BMC Biol. 2025 Jul 15;23(1):212. doi: 10.1186/s12915-025-02314-8.
Accurate identification of anti-inflammatory peptides (AIPs) is crucial for drug development and inflammatory disease treatment. However, the short length and limited informational content of peptide sequences make precise computational recognition particularly challenging. While various machine learning and deep learning approaches have been explored, their limitations in feature representation and model integration hinder the effective discovery of novel AIPs.
In this study, we present NeXtMD, a novel dual-module stacked framework that integrates both machine learning (ML) and deep learning (DL) components for accurate AIP identification. NeXtMD systematically extracts four functionally relevant sequence-derived descriptors-residue composition, inter-residue correlation, physicochemical properties, and sequence patterns-and utilizes a two-stage prediction strategy. The first stage generates preliminary predictions using four distinct encoding strategies and ML classifiers, while the second stage employs a multi-branch residual network (ResNeXt) to refine prediction outputs. Benchmark evaluations demonstrate that NeXtMD outperforms current state-of-the-art methods on multiple performance metrics. Moreover, NeXtMD maintains strong generalization capabilities when applied to unseen peptide sequences, showing its robustness and scalability.
NeXtMD offers a high-performance and interpretable computational framework for AIP identification, with significant potential to facilitate the discovery and design of peptide-based anti-inflammatory therapeutics. The architecture and methodological innovations of NeXtMD also provide a generalizable strategy that can be adapted to other bioactive peptide prediction tasks, supporting broader applications in therapeutic peptide development.
准确识别抗炎肽（AIPs）对于药物开发和炎症性疾病治疗至关重要。然而，肽序列的长度较短且信息内容有限，使得精确的计算识别极具挑战性。虽然已经探索了各种机器学习和深度学习方法，但它们在特征表示和模型整合方面的局限性阻碍了新型AIPs的有效发现。
在本研究中，我们提出了NeXtMD，这是一种新颖的双模块堆叠框架，它集成了机器学习（ML）和深度学习（DL）组件，用于准确识别AIPs。NeXtMD系统地提取了四个功能相关的序列衍生描述符——残基组成、残基间相关性、物理化学性质和序列模式，并采用两阶段预测策略。第一阶段使用四种不同的编码策略和ML分类器生成初步预测，而第二阶段采用多分支残差网络（ResNeXt）来优化预测输出。基准评估表明，NeXtMD在多个性能指标上优于当前的先进方法。此外，NeXtMD在应用于未见肽序列时保持强大的泛化能力，显示出其稳健性和可扩展性。
NeXtMD为AIP识别提供了一个高性能且可解释的计算框架，在促进基于肽的抗炎治疗药物的发现和设计方面具有巨大潜力。NeXtMD的架构和方法创新还提供了一种可推广的策略，可适用于其他生物活性肽预测任务，支持在治疗性肽开发中的更广泛应用。
