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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Protein Networks02:26

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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.
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Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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PreBINDS: An Interactive Web Tool to Create Appropriate Datasets for Predicting Compound-Protein Interactions.

Kazuyoshi Ikeda1,2, Takuo Doi3, Masami Ikeda4

  • 1Medicinal Chemistry Applied AI Unit, HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Yokohama, Japan.

Frontiers in Molecular Biosciences
|December 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a web server for creating custom compound-protein interaction (CPI) datasets. It aids researchers in developing and evaluating machine learning models for drug discovery and in silico screening.

Keywords:
CHEMBLcompound-protein interactiondatasetsdeep learninginteractive web servermachine learning

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning in drug discovery

Background:

  • Compound-protein interactions (CPIs) are crucial for drug discovery.
  • Developing accurate prediction models for CPIs requires high-quality, task-specific datasets.
  • Existing datasets may not always be suitable for diverse machine learning and deep learning approaches.

Purpose of the Study:

  • To develop a user-friendly web server for generating customized CPI datasets.
  • To facilitate the development and evaluation of machine learning models for CPI prediction.
  • To improve the accuracy and efficiency of in silico screening and drug design.

Main Methods:

  • Development of a web server utilizing ChEMBL database.
  • Implementation of customizable positive and negative thresholds for dataset generation.
  • Inclusion of graphical visualization for activity value distribution analysis.
  • Functionality for selecting target proteins based on Pfam families, ChEMBL classification, and sequence similarity.

Main Results:

  • A web server is available at https://binds.lifematics.work/ for public use.
  • Users can generate tailored CPI datasets by defining activity thresholds and protein selection criteria.
  • The server provides necessary attributes for deep learning model development.
  • Enhanced data preparation capabilities for machine learning in drug discovery.

Conclusions:

  • The developed web server simplifies the creation of appropriate CPI datasets.
  • It supports researchers in building and validating predictive models for drug discovery.
  • Improved dataset accessibility via the web server can enhance the performance of machine learning and deep learning in drug design.