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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Polyphony: superposition independent methods for ensemble-based drug discovery.

William R Pitt1, Rinaldo W Montalvão, Tom L Blundell

  • 1Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. will.pitt@ucb.com.

BMC Bioinformatics
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Summary

New methods analyze protein structures without superposition, revealing conformational changes and binding sites. This approach, implemented in the open-source Polyphony package, aids drug design and understanding protein dynamics.

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Biophysics

Background:

  • Structure-based drug design is iterative, involving structural biology, computational methods, and bioassays.
  • Large datasets of protein-ligand crystal structures and molecular dynamics (MD) simulations are increasingly common.
  • Current analysis methods for these ensembles often rely on least-squares superposition, which has limitations.

Purpose of the Study:

  • To develop novel, superposition-independent methodologies for analyzing ensembles of protein structures.
  • To enable the identification of key protein features like hinge regions, allosteric sites, and transient binding pockets.
  • To explore conformational changes and interactions within protein families.

Main Methods:

  • Statistical approaches based on residue equivalence, avoiding coordinate superposition.
  • Application to crystal structures of CDK2 and other CMGC protein kinases.
  • Analysis of molecular dynamics simulations, exemplified by p38α.

Main Results:

  • Identification of known and novel protein-ligand interaction-conformation relationships.
  • Prediction of a transient, druggable allosteric pocket in CDK2.
  • Discovery of an evolutionarily conserved link between this pocket and phosphorylation sites.

Conclusions:

  • Validated methodologies for superposition-independent conformational analysis of protein structure ensembles.
  • The Polyphony Python package provides an open-source implementation of these methods.
  • Facilitates deeper insights into protein conformational dynamics for drug discovery.