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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA has a double-helix structure. The...

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Related Experiment Video

Updated: Jul 2, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

RNALig: An ML-Driven Structure-Based Scoring Function for Estimating Binding Affinities of RNA-Ligand Complexes.

Priyanka Sharma1, N Latha1, Leena Aggarwal2

  • 1Department of Biotechnology, Bennett University, Greater Noida, Uttar Pradesh, India.

Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry
|July 1, 2026
PubMed
Summary

RNALig is a new machine learning tool that predicts how well small molecules bind to Ribonucleic acid (RNA). This computational approach aids in discovering new RNA-targeted drugs more efficiently.

Keywords:
RNA‐ligand binding affinityRNA‐targeted drug discoverybinding free energy predictionmachine learningstructure‐based scoring function

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07:35

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Published on: December 1, 2023

Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Ribonucleic acid (RNA) plays critical roles in gene regulation, catalysis, and disease.
  • Small molecules can target RNA function, making RNA a key therapeutic target.
  • Predicting RNA-ligand binding is essential for structure-guided drug discovery, but experimental methods are slow and costly.

Purpose of the Study:

  • To develop a machine learning (ML)-driven scoring function, RNALig, for accurate prediction of RNA-ligand binding free energies (ΔG).
  • To provide a structure-informed computational tool to accelerate RNA-targeted drug discovery.
  • To offer a transparent and generalizable framework for modeling RNA-ligand binding thermodynamics.

Main Methods:

  • Developed RNALig, a Random Forest Regressor model trained on 164 experimentally resolved RNA-ligand complexes.
  • Utilized three-dimensional (3D) structural and physicochemical descriptors, including RNA-specific, ligand-specific, and complex-level interaction features.
  • Validated the model on an independent test set of 70 complexes.

Main Results:

  • RNALig achieved high predictive performance with R² = 0.81 and RMSE = 0.64 kcal/mol.
  • The model outperformed existing methods like RSAPred (R² = 0.52) and DeepRSMA (R² = 0.67).
  • Demonstrated the integration of ML interpretability with structure-based descriptors for quantitative binding affinity prediction.

Conclusions:

  • RNALig provides a transparent, quantitative, and generalizable ML-driven framework for predicting RNA-ligand binding affinities.
  • The tool advances structure-guided RNA drug discovery by offering a more efficient alternative to experimental methods.
  • The RNALig pipeline and dataset are publicly available to facilitate further research.