Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

ATP and Macromolecule Synthesis

ATP and Macromolecule Synthesis

Biological macromolecules are organic compounds, predominantly composed of carbon atoms. The carbon atoms are covalently bonded with hydrogen, oxygen, nitrogen, and other minor elements. There are four major biological macromolecule classes: carbohydrates, lipids, proteins, and nucleic acids.
Most macromolecules are composed of single subunits, or building blocks, called monomers. The monomers combine with each other using covalent bonds to form larger molecules known as polymers.
Conversion of...

Nucleic Acid Structure

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

High-Throughput Fluorescence Profiling of Remodeling Dynamics at Surface-Deposited Amyloid Interfaces.

Analytical chemistry·2026

Same author

Efficient prime editing in vivo and in vitro using lipid nanoparticles.

Nature nanotechnology·2026

Same author

AI-guided redesign of laboratory-evolved reverse transcriptases enhances prime editing.

Nature biotechnology·2026

Same author

Directed evolution of small RNA-stabilizing motifs that improve prime-editing efficiency.

Nature biotechnology·2026

Same author

In vivo adenine base editing ameliorates Dravet syndrome phenotypes in a mouse model.

Science translational medicine·2026

Same author

In vivo base editing rescues liver pathophysiology and peroxisome dysfunction in a mouse model of Zellweger spectrum disorder.

Nature biomedical engineering·2026

Same journal

The BRCA1-A complex restricts replication fork reversal-dependent DNA repair in ATM deficient cells.

Nature communications·2026

Same journal

Signaling downstream of tumor-stroma interaction regulates mucinous colorectal adenocarcinoma apicobasal polarity.

Nature communications·2026

Same journal

Click-polymerized polyenamine membranes for efficient lithium extraction.

Nature communications·2026

Same journal

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026

Same journal

Proton shuttling at electrochemical interfaces under alkaline hydrogen evolution.

Nature communications·2026

Same journal

metilene<sup>3</sup>: identifying DMRs across multiple conditions with auto-classification.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 2, 2025

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

Published on: July 6, 2012

Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine

Jonathan C Chen^1,2,3, Jonathan P Chen^4,5, Max W Shen^1,2,3,6

¹Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Nature Communications

|August 4, 2022

Summary

This summary is machine-generated.

This study introduces a novel method combining in vitro selection and machine learning to discover functional biopolymers. The approach generates diverse, high-affinity nucleic acid polymers unrelated to initial discoveries, accelerating biopolymer engineering.

More Related Videos

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures

Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures

Published on: June 26, 2020

Related Experiment Videos

Last Updated: Sep 2, 2025

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles

Published on: July 6, 2012

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

Published on: February 6, 2020

Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures

Self-Assembly of Gamma-Modified Peptide Nucleic Acids into Complex Nanostructures in Organic Solvent Mixtures

Published on: June 26, 2020

Area of Science:

Biochemistry
Molecular Biology
Computational Chemistry

Background:

In vitro selection is crucial for discovering functional polymers from vast sequence spaces.
Exploring novel sequence space beyond experimental variants is challenging.
Limitations in selection and sequencing restrict the discovery of diverse functional biopolymers.

Purpose of the Study:

To develop an integrated approach combining in vitro selection and machine learning.
To discover novel highly side-chain-functionalized nucleic acid polymers (HFNAPs) with high affinity for daunomycin.
To generate diverse HFNAP sequences unrelated to experimentally derived variants using machine learning.

Main Methods:

In vitro selection was used to identify HFNAPs with daunomycin binding activity.
A conditional variational autoencoder (CVAE) machine learning model was trained on selection data.
The CVAE model generated novel HFNAP sequences with predicted high daunomycin affinities.

Main Results:

HFNAPs with potent daunomycin affinities (K_D = 5-65 nM) were discovered.
The CVAE model generated diverse HFNAP sequences with improved daunomycin affinities (K_D = 9-26 nM).
Generated sequences were unrelated to those obtained through experimental selection, expanding sequence space exploration.

Conclusions:

Coupling in vitro selection with machine learning enables efficient discovery of functional biopolymers.
This integrated approach allows for the direct generation of active variants beyond experimental limitations.
Demonstrates a powerful new strategy for biopolymer engineering and functional discovery.