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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Updated: Jun 15, 2025

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

Published on: November 21, 2013

Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine

Y Nissi Talluri¹, Subramanian Krs Sankaranarayanan^2,3, H Christopher Fry²

¹Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India.

Science Advances

|June 11, 2025

Summary

Artificial intelligence improves prediction of peptide self-assembly into beta sheets. Machine learning models identified novel peptide sequences with desired nanostructure assembly not found by traditional methods.

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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

Area of Science:

Biochemistry
Computational Biology
Materials Science

Background:

Predicting peptide secondary structure is difficult due to complex interactions and environmental factors.
Traditional peptide design methods can be biased, limiting the discovery of novel structures.

Purpose of the Study:

To improve the accuracy of predicting peptide self-assembly into beta sheets.
To discover unconventional peptide sequences with desired nanostructure assembly properties.

Main Methods:

Utilized an integrated high-throughput experimental workflow and an artificial intelligence-driven active learning framework.
Synthesized and tested 268 pentapeptides, focusing on sequences where machine learning predictions deviated from conventional strategies.

Main Results:

Identified 96 pentapeptides forming beta sheet assemblies.
Discovered unconventional sequences (e.g., ILFSM, LMISI) not predicted by traditional methods.
Developed machine learning models that outperformed conventional beta sheet propensity tables and revealed new chemical design rules.

Conclusions:

Machine learning-driven approaches overcome limitations of traditional peptide design.
The developed models and identified sequences facilitate the discovery of novel peptide nanostructures.
A web interface is available for community access to the predictive models.