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Related Concept Videos

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...
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...
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...
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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

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Related Experiment Video

Updated: Jun 11, 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

BioPipelines: Accessible Computational Protein and Ligand Design for Chemical Biologists.

Gianluca Quargnali1, Pablo Rivera-Fuentes1

  • 1Department of Chemistry, University of Zurich, 8057 Zurich, Switzerland.

Computational and Structural Biotechnology Journal
|June 10, 2026
PubMed
Summary

BioPipelines is a new Python framework simplifying computational protein design. It integrates over 40 tools for tasks like structure generation and sequence design, making advanced methods accessible to experimental labs.

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Last Updated: Jun 11, 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

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

  • Computational Biology
  • Protein Engineering
  • Drug Discovery

Background:

  • Deep learning advances protein engineering and drug discovery but faces adoption barriers due to complex computational requirements.
  • Experimental labs struggle with incompatible software, diverse formats, and high-performance computing needs.

Purpose of the Study:

  • To present BioPipelines, an open-source Python framework simplifying computational protein design workflows.
  • To enable researchers to easily define and execute multistep design processes.
  • To facilitate the integration of new functionalities, including coding agents.

Main Methods:

  • Developed an open-source Python framework (BioPipelines) for defining computational protein design workflows.
  • Integrated over 40 tools for structure generation, sequence design, prediction, screening, and analysis.
  • Enabled interactive prototyping in Jupyter notebooks and seamless transition to production-scale runs.

Main Results:

  • BioPipelines allows researchers to define complex workflows in a few lines of code.
  • The framework supports diverse applications including inverse folding, de novo protein design, and compound screening.
  • Demonstrated successful applications in gene synthesis, binding site optimization, and fusion-protein linker design.

Conclusions:

  • BioPipelines empowers researchers by abstracting computational complexities, allowing focus on scientific questions.
  • The framework's modularity and ease of use promote wider adoption of computational tools in chemical biology.
  • BioPipelines aims to accelerate protein engineering and drug discovery by streamlining design processes.