Assessing Scoring Functions for Protein−Ligand Interactions

Ferrara, Philippe; Gohlke, Holger; Price, Daniel J.; Klebe, G.; Brooks, Charles L.

doi:10.1021/jm030489h

Cited by 457 publications

(532 citation statements)

References 80 publications

Supporting

Mentioning

508

Contrasting

Unclassified

Order By: Relevance

“…The correctly predicted poses of NNI-3 and NNI-5 were obtained in the 50 output poses of Autodock, but they were not scored in the first position. In fact, it is well known that the accuracy of current scoring functions for small compounds is still poor [45,46]. Up to now, it is not possible to find a scoring function which can correlate quite well with the observed activities.…”

Section: Docking To the Representative Protein Structurementioning

confidence: 99%

Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Froeyen

Herdewijn

2009

J Mol Model

View full text Add to dashboard Cite

Modeling studies were performed on HCV NS5B Polymerase in an effort to design new inhibitors. The binding models of five different scaffold inhibitors were investigated and compared by using molecular dynamics simulations, free energy calculation and decomposition. Our results show Tyr448 plays the most critical role in the binding of most inhibitors. In addition, favorable contributions of residues Pro197, Arg200, Cys366, Met414 and Tyr448 in a deep hydrophobic pocket prove to be important for the selectivity of inhibitors. Furthermore, an optimized docking protocol was presented based on cross-docking the five inhibitors in the palm binding site of this enzyme using the Autodock program. This protocol was used later to virtually screen NCI and Maybridge diversity set libraries. The binding site was profiled via the statistics and analysis of the hydrogen bond networks formed between the receptor and the top-ranked diversity set compounds. Based on our detailed binding site analysis two useful rules were proposed to guide the selection of promising hits.Response to Reviewers: Question1: It is a pity that nothing in detail is discussed about found structures resulting from the virtual screening. Did the authors find new promising ligands with high (higher than the template ligands?) affinity and are they structurally diverse or similar to the used ones?Answer: We have found some promising compounds after the virtual screening. They are structurally diverse. Most of them form hydrogen bonds with the critical residues such as Tyr448 and have strong hydrophobic interaction with the residues in the deep hydrophobic pocket mentioned in our article. These compounds are under biological test.Question2: Additionally to figure 5 at least one figure should be added, showing the amino acid residues of the active site with the discussed most important interactions with one (the best?) ligand. This would considerably help to understand the discussion without looking in the original pdb-files. AbstractModeling studies were performed on HCV NS5B Polymerase in an effort to design new inhibitors. The binding models of five different scaffold inhibitors were investigated and compared by using molecular dynamics simulations, free energy calculation and decomposition. Our results show Tyr448 plays the most critical role in the binding of most inhibitors. In addition, favorable contributions of residues Pro197, Arg200, Cys366, Met414 and Tyr448 in a deep hydrophobic pocket prove to be important for the selectivity of inhibitors.Furthermore, an optimized docking protocol was presented based on cross-docking the five inhibitors in the palm binding site of this enzyme using the Autodock program. This protocol was used later to virtually screen NCI and Maybridge diversity set libraries. The binding site was profiled via the statistics and analysis of the hydrogen bond networks formed between the receptor and the top-ranked diversity set compounds. Based on our detailed binding site analysis two useful rules were proposed to ...

show abstract

Section: Docking To the Representative Protein Structurementioning

confidence: 99%

Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Froeyen

Herdewijn

2009

J Mol Model

View full text Add to dashboard Cite

show abstract

“…This approach is particularly attractive for the calculation of free energies of binding of macromolecular and small molecule-biomolecule complexes. 120,121 This type of approach should also be useful for the estimation of ligand-protein binding, 122 at a computationally reasonable cost as required for testing of large numbers of drug candidates.…”

Section: Treatment Of Solvationmentioning

confidence: 99%

Empirical force fields for biological macromolecules: Overview and issues

2004

View full text Add to dashboard Cite

Empirical force field-based studies of biological macromolecules are becoming a common tool for investigating their structure-activity relationships at an atomic level of detail. Such studies facilitate interpretation of experimental data and allow for information not readily accessible to experimental methods to be obtained. A large part of the success of empirical force field-based methods is the quality of the force fields combined with the algorithmic advances that allow for more accurate reproduction of experimental observables. Presented is an overview of the issues associated with the development and application of empirical force fields to biomolecular systems. This is followed by a summary of the force fields commonly applied to the different classes of biomolecules; proteins, nucleic acids, lipids, and carbohydrates. In addition, issues associated with computational studies on "heterogeneous" biomolecular systems and the transferability of force fields to a wide range of organic molecules of pharmacological interest are discussed.

show abstract

“…Then the solute experiences an effective potential energy (which already includes solvent entropic effects): (3) where U eff denotes an effective energy function that includes the solute potential energy and the interaction free energy (Gint). Gint itself is the sum of two components: (4) where G polar treats the electrostatic interactions of the solute with the solvent (i.e., using continuum electrostatics), and G nonpolar includes the nonpolar component of solvation. Then eq 2 reduces to (5) Here, the integrals over the solvent degrees of freedom are already incorporated into the effective potential energy U eff -(r u ).…”

Section: A Hydration Free Energies In Implicit Solventmentioning

confidence: 99%

Treating Entropy and Conformational Changes in Implicit Solvent Simulations of Small Molecules

2008

View full text Add to dashboard Cite

Implicit solvent models are increasingly popular for estimating aqueous solvation (hydration) free energies in molecular simulations and other applications. In many cases, parameters for these models are derived to reproduce experimental values for small molecule hydration free energies. Often, these hydration free energies are computed for a single solute conformation, neglecting solute conformational changes upon solvation. Here, we incorporate these effects using alchemical free energy methods. We find significant errors when hydration free energies are estimated using only a single solute conformation, even for relatively small, simple, rigid solutes. For example, we find conformational entropy (TΔS) changes of up to 2.3 kcal/mol upon hydration. Interestingly, these changes in conformational entropy correlate poorly (R 2 = 0.03) with the number of rotatable bonds. The present study illustrates that implicit solvent modeling can be improved by eliminating the approximation that solutes are rigid.

show abstract

Assessing Scoring Functions for Protein−Ligand Interactions

Cited by 457 publications

References 80 publications

Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Empirical force fields for biological macromolecules: Overview and issues

Treating Entropy and Conformational Changes in Implicit Solvent Simulations of Small Molecules

Contact Info

Product

Resources

About