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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...

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Protein Target Prediction and Validation of Small Molecule Compound
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Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Computational screening for active compounds targeting protein sequences: methodology and experimental validation.

Fei Wang1, Dongxiang Liu, Heyao Wang

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai, 201203, China.

Journal of Chemical Information and Modeling
|September 30, 2011
PubMed
Summary

This study introduces a novel sequence-based method for predicting drug-target interactions, enabling virtual screening without 3D protein structures. This approach successfully identified new drug candidates for key targets, advancing drug discovery.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • The majority of protein targets lack determined 3D structures and known ligands.
  • Predicting ligand-protein interactions without 3D structural information is crucial for drug discovery.
  • A general method for sequence-based interaction prediction would significantly accelerate the identification of novel therapeutics.

Purpose of the Study:

  • To develop a general method for predicting ligand-protein interactions using only protein primary sequences and small molecule structural features.
  • To demonstrate the efficacy of a sequence-based virtual screening approach in identifying novel active compounds.
  • To validate the model's potential for discovering ligands across diverse pharmacologically relevant targets.

Main Methods:

  • Utilized the support vector machine (SVM) approach to build a predictive model.
  • Trained the model on a dataset of 15,000 ligand-protein interactions involving 626 proteins and over 10,000 active compounds.
  • Employed sequence-based features for proteins and structural features for small molecules.

Main Results:

  • Successfully developed and validated a sequence-based model for predicting ligand-protein interactions.
  • Identified nine novel active compounds for four important targets: GPR40, SIRT1, p38, and GSK-3β.
  • This represents the first successful sequence-based virtual screening campaign.
  • The model demonstrated potential for discovering active ligands for any protein target.

Conclusions:

  • Sequence-based virtual screening is a viable and powerful approach for drug discovery, even without 3D structural data.
  • The developed SVM model offers a generalizable solution for identifying potential drug candidates across various protein targets.
  • This method has the potential to significantly reduce the time and cost associated with early-stage drug discovery.