Jun 26 2020In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs We pinpoint the key aspects for success such as the important role of key water molecules amino acid ionization states and the benefit of equilibration with specific ligands G-protein-coupled receptors (GPCRs) are crucial cell surface receptors that transmit signals from a wide range of extracellular ligands Indeed 40% to 50% of all marketed drugs are thought to modulate GPCR activity making them the major class of targets in the drug discovery process

Prediction of binding affinity and efficacy of thyroid

article{osti_22439864 title = {Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods} author = {Politi Regina and Department of Environmental Sciences and Engineering University of North Carolina Chapel Hill NC 27599 and Rusyn Ivan and Tropsha Alexander} abstractNote = {The thyroid hormone receptor (THR) is an

The consensus approach resulted in the experimental validation of 53% of the 2 R and 73% of the H 1 R virtual screening hits with up to nanomolar affinities and potencies The selective identification of 2 R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode

BindProfX is a renewed approach to assess protein-protein binding free-energy changes (ΔΔG) induced by single- and multiple-mutations This is an update on the BindProf method which was designed to calculate the protein binding free-energy from the multiple sequence alignments of interface structure profiles The major difference between BindProf and BindProfX is at the core algorithm to

processes and make excellent drug targets the prediction and consequent identification of GPCR bioactive ligands is a topic of high interest and active research GPCR ligands differ in shape size and physicochemical properties and include proteins peptides lipids steroids and other small organic molecules [6] Furthermore GPCR ligands

receptor (hD2DR) predicted from the primary sequence using ab initio theoretical and computational techniques 1 This 3-D structure was validated by predicting the binding site and relative binding affinities of dopamine plus 3 known dopamine receptor agonists (antiparkinsonian) and 8 known antagonists (antipsychotic) in the hD2DR receptor

The predicted 3D structure of the human D2 dopamine

techniques for predicting the 3D structure of GPCRs using only the amino acid sequence (MembStruk) and for predicting bind-ing site and binding energy of various ligands to GPCRs (Hier-Dock) Using these techniques we have reported the structure of olfactory receptors (3 4) bovine rhodopsin (4 5) and other GPCRs (4)

techniques for predicting the 3D structure of GPCRs using only the amino acid sequence (MembStruk) and for predicting bind-ing site and binding energy of various ligands to GPCRs (Hier-Dock) Using these techniques we have reported the structure of olfactory receptors (3 4) bovine rhodopsin (4 5) and other GPCRs (4)

Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs Such experiments as well as structure-activity relationships for series of ligands are usually interpreted with computationally derived models of ligand binding modes

ligands of orphan G-protein coupled receptors using bio-informatics This method is based on a machine learning approach recently introduced by the authors to estimate the binding free energy between a small-molecule ligand and a receptor protein 30 A distinct advantage of this approach is the simplicity of requisite input data: proteins are

BindProf is a method for predicting free energy changes (ΔΔG) of protein-protein binding interactions upon mutations of residues at the interface While BindProf adopts a multi-scale approach using multiple sources of information at different levels of structural resolution

Oct 01 2002G protein-coupled receptors (GPCRs) mediate our sense of vision smell taste and pain They are also involved in cell recognition and communication processes and hence have emerged as a prominent superfamily for drug targets Unfortunately the atomic-level structure is available for only one GPCR (bovine rhodopsin) making it difficult to use structure-based methods to design drugs and

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction as they provide an intermediate compromise of speed and accuracy between the

Aug 02 2002The present review summarizes our current understanding of the binding affinity of a small‐molecule ligand to a protein Both theoretical and empirical approaches for predicting binding affinity starting from the three‐dimensional structure of a

Frontiers

Binding Curve Figure 2 shows a hypothetical example of a binding curve for two ligands: Ligand 1 and Ligand 2 The x-axis represents the concentration of the ligand and the y-axis represents the percentage of available binding sites (Θ) in a protein that is occupied by the ligand The values of Θ range from 0 to 1 (corresponding to the range from 0 to in Figure 2)

Binding affinity is most commonly determined using a radiolabeled ligand known as a tagged ligand Homologous competitive binding experiments involve binding competition between a tagged ligand and an untagged ligand Real-time based methods which are often label-free such as surface plasmon resonance dual-polarization interferometry and multi-parametric surface plasmon resonance (MP

Mar 30 2016On the set of 24 MHC alleles characterized with 20 or more ligand data in the SYFPEITHI database we predicted binding affinity values for 1 000 000 random natural peptides with a length of 8–11 amino acids (250 000 peptides for each length) using the allmer and 9mer models (using the L-mer approximation to predict binding for non-9mer peptides)

Jan 28 2014G-protein-coupled receptors (GPCRs) play fundamental roles in most physiological processes by modulating diverse signaling pathways and thus have become one of the most important drug targets Based on the fact that a variety of the extracellular signals are mediated in a ligand-specific manner such as inverse agonist neutral antagonist and agonist quantitative characterization of the

Jun 26 2020In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs We pinpoint the key aspects for success such as the important role of key water molecules amino acid ionization states and the benefit of equilibration with specific ligands

Binding Curve Figure 2 shows a hypothetical example of a binding curve for two ligands: Ligand 1 and Ligand 2 The x-axis represents the concentration of the ligand and the y-axis represents the percentage of available binding sites (Θ) in a protein that is occupied by the ligand The values of Θ range from 0 to 1 (corresponding to the range from 0 to in Figure 2)

Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design In recent years several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein However ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for