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Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:22

Protein Folding

Overview
Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...

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Related Experiment Video

Updated: Jun 1, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

InversePep: Diffusion-driven structure-based inverse folding for functional peptides.

Srinivas Kashyap Chilakamarri1,2, Sneha Reddy Kasturi1,2, Sai Pranav Reddy Yerrabandla2,3

  • 1Department of Computer Science and Engineering, Keshav Memorial Engineering College, Uppal, Hyderabad, Telangana 500088, India.

Briefings in Bioinformatics
|May 31, 2026
PubMed
Summary
This summary is machine-generated.

InversePep, a new generative diffusion model, designs functional peptides by learning sequence-structure relationships. This approach enables the creation of stable, diverse peptides for therapeutic applications.

Keywords:
GVP-GNNdiffusioninverse foldingpeptidessequence predictiontransformers

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Published on: December 30, 2016

Area of Science:

  • Computational biology
  • Peptide design
  • Bioinformatics

Background:

  • Designing functional peptides is crucial for protein engineering and drug discovery.
  • Existing methods struggle with peptide-specific challenges like flexibility and short length.
  • Structure-based inverse folding models perform poorly on peptides.

Purpose of the Study:

  • To develop a novel generative model for structure-based peptide inverse folding.
  • To generate peptides with specific structural geometries and desired properties.
  • To overcome limitations of current peptide design approaches.

Main Methods:

  • Introduced InversePep, a generative diffusion model for peptide inverse folding.
  • Integrated a geometric graph neural network and a Transformer-based sequence refinement module.
  • Trained the model on diverse peptide backbones from Propedia and SATPdb.

Main Results:

  • InversePep effectively captures peptide structural and biochemical diversity.
  • Outperformed existing models like ProteinMPNN and ESM-IF1 in generating geometry-consistent peptide sequences.
  • Achieved strong performance metrics including Mean TM-SCORE of 0.51 and Median RMSD-Simple of 0.97.
  • In-silico folding confirmed that generated peptides adopt target conformations.

Conclusions:

  • InversePep enables the design of structurally stable and sequence-diverse peptides.
  • Demonstrates significant potential for applications in antimicrobial peptide discovery and peptide therapeutics.
  • Represents a breakthrough in structure-based peptide design.