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Peptide Bonds

Peptide Bonds

A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...

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Related Experiment Video

Updated: Jun 20, 2026

Constructing Thioether/Vinyl Sulfide-tethered Helical Peptides Via Photo-induced Thiol-ene/yne Hydrothiolation

Constructing Thioether/Vinyl Sulfide-tethered Helical Peptides Via Photo-induced Thiol-ene/yne Hydrothiolation

Published on: August 1, 2018

AI-Designed Peptides as Tools for Biochemistry.

Lauren Hong¹, Sophia Vincoff¹, Pranam Chatterjee^1,2

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

|April 10, 2026

Summary

This summary is machine-generated.

Artificial intelligence is revolutionizing biochemistry by enabling the rational design of custom peptides. These AI-designed peptides offer precise control over protein function, overcoming historical limitations in peptide tool development.

Keywords:

AI-designed peptides modern biochemical research sequence- and structure-based design paradigms

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Last Updated: Jun 20, 2026

Constructing Thioether/Vinyl Sulfide-tethered Helical Peptides Via Photo-induced Thiol-ene/yne Hydrothiolation

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Published on: August 1, 2018

A Tripeptide-Stabilized Nanoemulsion of Oleic Acid

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

Biochemistry
Computational Biology
Molecular Biology

Background:

Peptides are valuable biochemical tools due to their modularity and precision in perturbing protein function.
Traditional peptide discovery faced challenges including slow workflows, limited specificity control, and poor physicochemical properties.
Recent advancements in artificial intelligence (AI) are transforming peptide design and application.

Purpose of the Study:

To review AI-designed peptides as practical tools for modern biochemical research.
To highlight sequence-based and structure-based AI design paradigms for peptides.
To provide a framework for selecting and deploying AI-designed peptides based on experimental utility and design constraints.

Main Methods:

Survey of sequence-based AI design paradigms for peptide generation.
Survey of structure-based AI design paradigms for peptide generation.
Analysis of AI-designed peptide applications in biochemistry.

Main Results:

AI enables rational, data-driven design of peptides for specific experimental tasks.
AI-designed peptides include isoform-specific binders, motif-specific binders, and multi-objective reagents.
Functional peptides designed by AI can mediate protein degradation, stabilization, or biophysical interrogation.

Conclusions:

AI-designed peptides represent a significant advancement in biochemical tool development.
These peptides offer enhanced precision, specificity, and tailored properties for research applications.
A framework for selecting and deploying AI-designed peptides is crucial for their effective use in modern biochemistry.