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Related Concept Videos

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...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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...

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials.

Aarti Garg1, Rupinder Tewari, Gajendra P S Raghava

  • 1Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India.

BMC Bioinformatics
|March 13, 2010
PubMed
Summary

This study developed quantitative structure activity relationship (QSAR) models to predict potent inhibitors for dihydrodipicolinate synthase (DHDPS), an essential bacterial enzyme. The developed KiDoQ webserver aids in discovering novel antibacterial agents by predicting inhibitor values.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Identifying novel drug targets and inhibitors is crucial for drug design and development.
  • The diaminopimelic acid (DAP) pathway is essential for bacterial survival and cell wall biosynthesis, but absent in mammals.
  • Dihydrodipicolinate synthase (DHDPS) is a key enzyme in the DAP pathway, making it a promising target for antibacterial drug development.

Purpose of the Study:

  • To develop a methodology for predicting novel and potent inhibitors against DHDPS.
  • To design effective antibacterial agents by targeting the DHDPS enzyme.
  • To provide a computational tool for experimentalists to discover new DHDPS inhibitors.

Main Methods:

  • Quantitative Structure-Activity Relationship (QSAR) models were developed using experimentally verified DHDPS inhibitors.
  • Molecular docking was performed using AutoDock to identify energy-based descriptors.
  • Multiple Linear Regression (MLR) and Support Vector Machine (SVM) models were trained and validated using cross-validation techniques.

Main Results:

  • The SVM-based QSAR model achieved high performance with R/q2 values of 0.93/0.80 and MAE of 1.89.
  • External cross-validation demonstrated robust model performance with high training/testing correlation values (q2/r2) ranging from 0.78-0.83/0.93-0.95.
  • Eleven energy-based descriptors were identified through docking, contributing to QSAR model development.

Conclusions:

  • QSAR modeling of ligand-receptor binding interactions for DHDPS is a promising approach for predicting antibacterial agents.
  • A webserver named "KiDoQ" (http://crdd.osdd.net/raghava/kidoq) has been developed for predicting the inhibitory values (Ki) of new ligand molecules against DHDPS.
  • The KiDoQ webserver can assist experimentalists in the development of novel and potent antibacterial inhibitors.