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Related Concept Videos

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...
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...
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Peptide Identification Using Tandem Mass Spectrometry01:33

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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Related Experiment Video

Updated: May 25, 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

Predicting lasso peptide structure with LassoPred.

Xingyu Ouyang1, Xinchun Ran1, Dantong Zhu1

  • 1Department of Chemistry, Vanderbilt University, Nashville, TN, United States.

Methods in Enzymology
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

LassoPred accurately predicts complex lasso peptide structures from sequence alone using machine learning. This tool expands structural coverage, aiding therapeutic design and synthetic biology applications.

Keywords:
Lasso peptide structure predictionLassoPredMachine learning

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Last Updated: May 25, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Published on: January 26, 2024

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Biochemistry and Structural Biology
  • Computational Biology and Bioinformatics
  • Synthetic Biology and Drug Discovery

Background:

  • Lasso peptides (LaPs) are a unique class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with a stable, threaded rotaxane topology and diverse bioactivities.
  • Accurate 3D structure prediction of LaPs is challenging due to limited homologous templates and the inability of standard tools to model their knotted structures.
  • Despite their therapeutic potential, the structural diversity of LaPs remains underexplored, hindering further research and development.

Purpose of the Study:

  • To develop a computational pipeline, LassoPred, for high-throughput 3D structure prediction of lasso peptides solely from their amino acid sequences.
  • To address the limitations of existing prediction tools in accurately modeling the unique topology of LaPs.
  • To expand the structural coverage of known lasso peptides and facilitate their application in various biological and therapeutic fields.

Main Methods:

  • LassoPred integrates machine learning, specifically support vector machine classifiers trained on ESM2 embeddings, to identify key topological features like the isopeptide ring and plug residues.
  • A topology-aware structure constructor utilizes homology modeling, residue mutation, and energy minimization to assemble and refine atomic models.
  • The pipeline is implemented in Python and accessible via a public web interface, supporting compatibility with AMBER and PyMOL.

Main Results:

  • LassoPred achieves near-experimental accuracy in predicting lasso peptide structures.
  • The prediction time is significantly reduced to minutes, enabling rapid analysis.
  • Structural coverage of lasso peptides is expanded from fewer than 50 experimentally determined structures to over 4000 genome-mined models.
  • The pipeline is extensible to engineered LaP variants and other RiPP families.

Conclusions:

  • LassoPred effectively bridges the gap between lasso peptide sequence discovery and structural insight.
  • The tool democratizes structural analysis of LaPs for both expert and non-specialist users.
  • This advancement facilitates downstream applications in enzymology, structural biology, synthetic biology, and therapeutic design.