Beyond the Folded and the Small: Computational Approaches to Disordered Proteins, Nucleic Acids, and Mutation-Driven Disease Mechanisms

Computational structural biology is a rapidly advancing field that leverages theoretical, statistical, and computational methods to uncover the molecular basis of biological function. While recent breakthroughs in molecular dynamics simulations and macromolecular structure prediction have profoundly enhanced our understanding of well-folded biomolecular systems, major challenges remain in accurately describing conformational dynamics, modeling intrinsically disordered proteins (IDPs), capturing the structural behavior of nucleic acids such as ribozymes and DNAzymes, and predicting the impact of disease-linked mutations on protein and ion channel function. The incorporation of artificial intelligence (AI) and machine learning (ML) has opened unprecedented opportunities to overcome these computational limitations, automate feature recognition from complex datasets, and accelerate predictive modeling of these harder-to-describe systems. Despite these advances, further research is needed to refine ML-based models, improve their interpretability, and ensure reliable integration with experimental data to achieve deeper biological insight.This Research Topic aims to explore the confluence of computational, AI-driven, and simulation-based approaches to study the structure, dynamics, and interactions of disordered proteins, nucleic acids, and mutation-affected macromolecules. The overarching goal is to deepen our understanding of conformational dynamics in IDPs and their complexes, elucidate the structural behavior of RNA, DNA, and catalytic nucleic acids, and clarify how genetic mutations reshape protein and ion channel structure and function in disease. Ultimately, this Topic seeks to highlight studies that bridge the gap between modeling and molecular function for these irregular and dynamic systems, contributing to translational applications in biomedical research.This Research Topic encompasses interdisciplinary studies at the interface of computational biology, structural biochemistry, and systems pharmacology. It covers methodological innovations, software development, and data-driven discoveries related to the simulation of disordered, nucleic acid, and mutation-affected systems. To gather further insights in computational structural biology and its biomedical applications, we welcome articles addressing, but not limited to, the following themes:- Simulation studies of intrinsically disordered proteins and their complexes- Machine learning-enhanced molecular dynamics simulations of proteins and nucleic acids- Modeling and simulation of DNA, RNA, ribozymes, and DNAzymes- Advances in trajectory analysis and novel ML-based simulation algorithms for irregular and dynamic systems- Simulations of genetic mutations and their implications for disease mechanisms- Modeling structure-function relationships in mutated ion channels- Molecular docking and evaluation of drug interactions with mutated proteins- AI and ML approaches in modeling protein-ligand interactions involving disordered or mutated targets- Computational modeling and target validation in drug discovery for disordered and mutation-linked systems Topic editors: Tianfang Wang, Harunur Rashid, N. Prakash Prabhu Source: https://www.frontiersin.org/research-topics/81437/beyond-the-folded-and-the-small-computational-approaches-to-disordered-proteins-nucleic-acids-and-mutation-driven-disease-mechanisms