REVIEW article

Front. Mol. Biosci.

Sec. Biological Modeling and Simulation

Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1542267

This article is part of the Research TopicDecoding the Conformation of Intrinsically Disordered Proteins: A Deep Learning ApproachView all articles

Use of AI-Methods over MD Simulations in the Sampling of Conformational Ensembles in IDPs

Provisionally accepted
Souradeep  SilSouradeep Sil1Ishita  DattaIshita Datta2Sankar  BasuSankar Basu3*
  • 1Osmania University, Hyderabad, Telangana, India
  • 2Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • 3Asutosh College, University of Calcutta, Kolkata, India

The final, formatted version of the article will be published soon.

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.

Keywords: intrinsically disordered proteins, conformational sampling, deep learning, artificial intelligence, MD simulations

Received: 09 Dec 2024; Accepted: 17 Mar 2025.

Copyright: © 2025 Sil, Datta and Basu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Sankar Basu, Asutosh College, University of Calcutta, Kolkata, India

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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