Sil Souradeep, Datta Ishita, Basu Sankar
Department of Genetics, Osmania University, Hyderabad, India.
Department of Genetics and Plant Breeding, Banaras Hindu University, Varanasi, India.
Front Mol Biosci. 2025 Apr 8;12:1542267. doi: 10.3389/fmolb.2025.1542267. eCollection 2025.
Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these ensembles is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive and struggle to sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient and scalable conformational sampling. They leverage large-scale datasets to learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of conformational ensembles in IDPs without the constraints of traditional physics-based approaches. Such DL approaches have been shown to outperform MD in generating diverse ensembles with comparable accuracy. Most models rely primarily on simulated data for training and experimental data serves a critical role in validation, aligning the generated conformational ensembles with observable physical and biochemical properties. However, challenges remain, including dependence on data quality, limited interpretability, and scalability for larger proteins. Hybrid approaches combining AI and MD can bridge the gaps by integrating statistical learning with thermodynamic feasibility. Future directions include incorporating physics-based constraints and learning experimental observables into DL frameworks to refine predictions and enhance applicability. AI-driven methods hold significant promise in IDP research, offering novel insights into protein dynamics and therapeutic targeting while overcoming the limitations of traditional MD simulations.
内在无序蛋白质（IDP）以动态集合而非稳定的三级结构形式存在，这对传统的结构-功能范式提出了挑战。捕获这些集合对于理解它们的生物学作用至关重要，然而分子动力学（MD）模拟虽然准确且被广泛使用，但计算成本高昂，并且难以对罕见的瞬态进行采样。人工智能（AI）提供了一种变革性的替代方法，深度学习（DL）能够实现高效且可扩展的构象采样。它们利用大规模数据集来学习复杂的、非线性的序列到结构的关系，从而能够在不受传统基于物理方法限制的情况下对IDP中的构象集合进行建模。事实证明，这种DL方法在生成具有可比准确性的多样集合方面优于MD。大多数模型主要依赖模拟数据进行训练，而实验数据在验证中起着关键作用，使生成的构象集合与可观察到的物理和生化特性相一致。然而，挑战仍然存在，包括对数据质量的依赖、有限的可解释性以及对更大蛋白质的可扩展性。将AI和MD相结合的混合方法可以通过将统计学习与热力学可行性相结合来弥补这些差距。未来的方向包括将基于物理的约束和学习实验可观察量纳入DL框架，以改进预测并增强适用性。AI驱动的方法在IDP研究中具有巨大的潜力，在克服传统MD模拟局限性的同时，为蛋白质动力学和治疗靶点提供了新的见解。
