XNA-Hub Platform

The XNA-Hub Platform is a pioneering computational platform developed by Nostrum Biodiscovery, dedicated to the modeling, design, and optimization of both natural and non-natural nucleic acids with pharmaceutical interest. It is purpose-built for researchers and drug developers working across a broad spectrum of nucleic acid modalities — including antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), messenger RNAs (mRNAs), aptamers, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and splicing stabilizers — enabling cutting-edge solutions for gene regulation, protein expression, and precision-targeted therapies.

By integrating advanced computational tools, classical and enhanced molecular dynamics (MD) simulations, free energy calculations, and AI-driven methodologies, XNA-Hub accelerates the discovery and optimization of next-generation nucleic acid therapeutics. The platform is designed for teams that require rigorous structural insight, thermodynamic profiling, and chemical engineering capabilities across RNA and DNA drug programs.

Core Platform Capabilities

• 2D and 3D Structure Prediction and Modeling: Accurate prediction and visualization of nucleic acid structures, including chemically modified sequences.
• Automatic Parameterization of Chemical Modifications: Seamlessly incorporates non-natural nucleotides into computational workflows to evaluate novel therapeutic designs.
• Plain and Biased MD Simulations: Generates informative trajectories to characterize statistically significant local and global observables.
• Free Energy Calculations: Provides insights into the stability and dynamics of nucleic acids for enhanced predictive capabilities.

Applications of XNA-Hub

• Design of next-generation RNA and DNA therapeutics.
• Exploration of novel chemical modifications for nucleic acid drug candidates.
• Comprehensive analysis of RNA interactions within protein- or ligand-mediated complexes to uncover binding mechanisms, structural dynamics, and functional implications.

RNA-Ligand Modeling

• Simulation of RNA-ligand complexes to characterize structural behavior.
• Prediction of binding free energy for RNA-ligand interactions.
• Prediction of binding kinetics, including kon and koff rates.
• Optimization and improvement of RNA-ligand interactions for therapeutic design.

Vaccines and Lipid Nanoparticles (LNPs)

• mRNA Stabilization: Computational strategies to enhance mRNA half-life and translational efficiency.
• Delivery Optimization: Lipid nanoparticle (LNP) and other advanced delivery systems designed for targeted immune response.
• Lipid Nanoparticle Formulation: Custom lipid compositions for optimal encapsulation of nucleic acid payloads.
• Conjugation: Development of conjugated mRNA platforms for novel immunotherapeutic applications.

Force Fields and Parameterization

• ParmBSC2: A state-of-the-art force field for natural and xeno nucleic acids, built on advanced quantum mechanics and machine learning, enabling precise modeling of DNA and RNA dynamics and complex structures. Extensively validated against experimental data and optimized for integration with leading molecular dynamics platforms.
• Ad-hoc Parameterization: Custom parameter generation for unique nucleic acid chemistries.
• Multilevel Parametrization: Bridging classical molecular mechanics and quantum-based methods for comprehensive coverage.
• Biophysics Validation: Rigorous validation through both experimental and computational approaches.

Analysis and Outcomes

• Thermodynamics: Precise calculations of stability and free energy changes across nucleic acid systems.
• Folding: In-depth analysis of secondary and tertiary RNA and DNA structures.
• Binding Affinity: Computational prediction and experimental validation of nucleic acid interactions.
• RNA-Protein Interaction: Characterizing functional binding and co-regulation mechanisms.
• RNA-RNA Interaction: Modeling complex regulatory networks involving RNA species.
• DNA-RNA Interaction: Exploring hybridization and transcription dynamics.
• Reverse Engineering: Deconvoluting sequence-function relationships for therapeutic applications.
• Target Recognition: High-throughput prediction of nucleic acid-target binding specificity.

XNA-Hub leverages high-performance computing infrastructure, combining classical MD simulations with enhanced sampling methods and AI tools to analyze key events such as target recognition, binding interactions, and chemical engineering. The platform is supported by an extensive publication record in leading journals including Nucleic Acids Research, Nature Communications, ACS Publications, and Cell Press, reflecting the scientific rigor underpinning its methodologies.