Computational Analysis of Protein and Nucleic Acid Structures, Interactions, and Functions
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 1318
Special Issue Editors
Interests: theoretical and computational method development; molecular dynamics simulations; machine learning; protein-nucleic acids complexes; enzymatic reactions; drug design
Special Issues, Collections and Topics in MDPI journals
Interests: computer aided drug design; molecular dynamics simulations of biological molecules; ion channel; membrane biophysics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue presents cutting-edge computational approaches for analyzing protein and nucleic acid structures, as well as their dynamic interactions and functional implications. Recent breakthroughs in artificial intelligence and high-performance computing have led to transformative advances in biomolecular modeling and prediction. This collection of studies features pioneering integrations of AI and molecular dynamics, including neural network-enhanced force fields for accelerated conformational sampling and hybrid architectures that combine deep learning predictions with physics-based simulations. Highlighted innovations include 3D structure determination (cryo-EM and AI-based folding), molecular dynamics simulations of conformational changes in challenging biomolecules, and network-based analysis of biological macromolecule interactions. Emphasis is placed on integrative methods that combine structural biology data with multi-omics datasets to reveal mechanistic insights into cellular processes. Emerging applications in drug design (targeting protein–nucleic acid interfaces) and synthetic biology demonstrate these technologies' translational potential. Key themes include (1) next-generation prediction tools (AlphaFold3 and RoseTTA-NA), (2) AI-optimized analysis of binding thermodynamics/kinetics, (3) multi-scale modeling from the atomic level to cellular level, and (4) high-throughput screening of functional variants. These works collectively showcase computational biology's capacity to reveal biomolecular mechanisms—from allosteric regulation to small-molecule and/or nucleic acid therapeutics—while providing both methodological benchmarks and future roadmaps for computational biophysics and biomedical research.
Dr. Wenjin Li
Dr. Ruoxu Gu
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- molecular dynamics simulations
- multi-scale modeling
- protein–nucleic acid interactions
- protein and/or nucleic acid structure prediction
- binding thermodynamics/kinetic
- AI-assisted drug design
- functional prediction
- network-based functional analysis
- multi-omics integration
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