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Related Concept Videos

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.
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Protein-Drug Binding: Mechanism and Kinetics01:16

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

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Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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DROIDS 3.0-Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics.

Gregory A Babbitt1, Ernest P Fokoue2, Joshua R Evans1

  • 1Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.

Biophysical Journal
|January 14, 2020
PubMed
Summary

We introduce DROIDS 3.0, a new method and software for analyzing protein dynamics using machine learning. This tool helps understand how genetic and drug variations affect protein function by analyzing large simulation data.

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Area of Science:

  • Bioinformatics and computational biology
  • Protein dynamics and function analysis
  • Statistical inference in molecular simulations

Background:

  • Molecular dynamics (MD) simulations generate complex big data, posing interpretation challenges for biomolecular function studies.
  • Investigating protein function requires statistical methods beyond sequence and structure analysis, especially for chaotic MD trajectories.
  • Large ensembles of simulations are needed to represent comparative functional states of proteins for robust statistical inference.

Purpose of the Study:

  • To present Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics.
  • To introduce maxDemon 1.0, a machine learning application for classifying protein functional states from MD simulations.
  • To enable analysis of genetic and drug variant impacts on conserved protein dynamics.

Main Methods:

  • Utilizing large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS.
  • Training maxDemon 1.0 on these ensembles to classify protein functional states.
  • Employing local canonical correlations in MD validation runs to identify functionally conserved protein dynamics.
  • Deploying trained classifiers on variant MD simulations to quantify impacts of genetic and drug variations.

Main Results:

  • Demonstrated accurate identification of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP) using the machine learning algorithm.
  • Quantified the impact of genetic variation in TBP and drug variations on Hsp90's ATP-binding region on conserved dynamics.
  • Identified conserved dynamics in Hsp90 linking the ATP-binding pocket to other functional regions.
  • Showcased that Hsp90 inhibitor impacts correlate with their mimicry of natural ATP binding.

Conclusions:

  • DROIDS 3.0 and maxDemon 1.0 provide a powerful framework for comparative protein dynamics analysis.
  • The developed machine learning approach effectively identifies conserved dynamics and quantifies variant impacts.
  • This method advances the understanding of protein function, genetic variations, and drug interactions through MD simulation analysis.