Parameter Refinement for Molecular Docking

Salo, Jukka-Pekka; Yliniemelä, and Ari; Taskinen, Jyrki

doi:10.1021/ci9801825

Cited by 7 publications

(9 citation statements)

References 37 publications

(124 reference statements)

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“…The molecular surface of urease was calculated using a probe atom with a radius of 1.0 Å and a dot density of 8 points/Å. A radius of 0.83 Å was used for the Ni(II) ions .…”

Section: Materials and Methodsmentioning

confidence: 99%

Urease Inhibition in the Presence of N-(n-Butyl)thiophosphoric Triamide, a Suicide Substrate: Structure and Kinetics

et al. 2017

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The nickel-dependent enzyme urease is a virulence factor for a large number of pathogenic and antibiotic-resistant bacteria, as well as a negative factor for the efficiency of soil nitrogen fertilization for crop production. The use of urease inhibitors to offset these effects requires knowledge, at a molecular level, of their mode of action. The 1.28 Å resolution structure of the enzyme-inhibitor complex obtained upon incubation of Sporosarcina pasteurii urease with N-(n-butyl)thiophosphoric triamide (NBPT), a molecule largely utilized in agriculture, reveals the presence of the monoamidothiophosphoric acid (MATP) moiety, obtained upon enzymatic hydrolysis of the diamide derivative of NBPT (NBPD) to yield n-butyl amine. MATP is bound to the two Ni(II) ions in the active site of urease using a μ-bridging O atom and terminally bound O and NH groups, with the S atom of the thiophosphoric amide pointing away from the metal center. The mobile flap modulating the size of the active site cavity is found in the closed conformation. Docking calculations suggest that the interaction between urease in the open flap conformation and NBPD involves a role for the conserved αArg339 in capturing and orienting the inhibitor prior to flap closure. Calorimetric and spectrophotometric determinations of the kinetic parameters of this inhibition indicate the occurrence of a reversible slow inhibition mode of action, characterized, for both bacterial and plant ureases, by a very small value of the dissociation constant of the urease-MATP complex. No need to convert NBPT to its oxo derivative NBPTO, as previously proposed, is necessary for urease inhibition.

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“…The molecular surface of urease was calculated using a probe atom with a radius of 1.0 Å and a dot density of 8 points/Å. A radius of 0.83 Å was used for the Ni(II) ions .…”

Section: Materials and Methodsmentioning

confidence: 99%

Urease Inhibition in the Presence of N-(n-Butyl)thiophosphoric Triamide, a Suicide Substrate: Structure and Kinetics

et al. 2017

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“…In recent decades design of experiments (DoE) 1, 2 has proved to be highly valuable in many fields of pharmaceutical research, including synthesis of molecule libraries, development of analytical methods and quantitative structure activity/property relationship modeling 3–5. However, it is still rarely applied in computer‐based drug research, including docking and virtual screening (VS) investigations, although the potential utility of DoE in such activities has been reported in several studies 6–9. Here, we present a methodology based on DoE for the systematic and thorough identification and analysis of factors influencing the results of docking‐based VS. Two fundamentally different software packages were used in our exploration of influential factors: FRED 10 and GOLD 11–13.…”

Section: Introductionmentioning

confidence: 99%

“…Known sources of variation in docking results include the settings of docking parameters (parameters that the user can alter influencing, for example, the pose sampling) and choice of post‐docking scoring functions for ranking the docked molecules 6, 9, 26, 30, 37, 41–43. In FRED 10, docking parameters control the positioning of the molecules in the active site, optimization of the binding pose and assessment of fitness of individual conformations ( exhaustive scoring ).…”

Section: Introductionmentioning

confidence: 99%

Multivariate assessment of virtual screening experiments

Andersson¹,

Chen²,

Linusson³

2010

Journal of Chemometrics

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Discovering molecules with a desired biological function is one of the great challenges in drug research. To discover new lead molecules, virtual screens (VS) are often conducted, in which databases of molecules are screened for potential binders to a specific protein, using molecular docking. The choice of docking software and parameter settings within the software can significantly influence the outcome of a VS. In this study, we have applied chemometric methods such as design of experiments, principal component analysis and partial least-square projections to latent structure (PLS) to simulated VS experiments to find and compare suitable conditions for performing VS against six protein targets selected from the DUD databases. The docking parameters in FRED, and scoring functions in both FRED and GOLD docking software, were varied according to a statistical experimental design and a PLS model was calculated to correlate the experimental setup to the VS outcome. The study revealed that the choice of scoring function has the greatest influence on VS outcome, and that other parameters have varying influence, depending on the protein target. We also found that substantial bias can be introduced by the lack of variation of molecular properties in the databases used in the screening. Our results provide indications that docking experiments could be tailored to the protein target in order to obtain satisfactory VS results.

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“…11 Antes et al applied a "design of experiment" strategy to tune empirical scoring functions to kinases and ATPases. 12 Similarly, Salo et al 13 and Andersson et al 14 used experimental design strategies for optimizing protein-ligand docking parameters. Another very promising approach has been proposed by Pham and Jain, who used negative training data, that is, the knowledge not only of binding molecules but additionally of nonbinding molecules, to optimize the generic Surflex scoring function.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Signal-to-Noise Ratio of Scoring Functions for Protein−Ligand Docking

Seifert¹

2008

J. Chem. Inf. Model.

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Empirical scoring functions provide estimates of the free energy of protein-ligand binding in situations when atomic-scale simulations are intractable, for example, in virtual high-throughput screening. Currently, such scoring functions are often inaccurate, and further improvements are complicated by the lack of reliable training data, the complex interplay between scoring functions and docking algorithms, and an inconsistent statistical treatment of positive and negative training data. In comparison to various other performance measures of scoring functions, "analysis of variance" provides a well-behaved objective function for optimization, which focuses on the signal-to-noise ratio of ligand-decoy discrimination. In combination with a large database of ligands and decoys, an in situ optimization of scoring function parameters was able to generate improved, target-specific scoring functions for three different proteins of pharmaceutical interest: cyclin-dependent kinase 2, the estrogen receptor, and cyclooxygenase-2. Statistical analysis of the improvements observed in "receiver-operating characteristic" curves showed that the optimized scoring functions achieved a significantly (between p < 0.0001 and p < 0.05) higher enrichment of true ligands. A scaffold dependence of the resulting binding modes was observed, which is discussed in conjunction with the rigid receptor hypothesis commonly made in protein-ligand docking. In summary, the approach described here represents a well-adapted statistical method for setting up scoring functions.

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Parameter Refinement for Molecular Docking

Cited by 7 publications

References 37 publications

Urease Inhibition in the Presence of N-(n-Butyl)thiophosphoric Triamide, a Suicide Substrate: Structure and Kinetics

Urease Inhibition in the Presence of N-(n-Butyl)thiophosphoric Triamide, a Suicide Substrate: Structure and Kinetics

Multivariate assessment of virtual screening experiments

Optimizing the Signal-to-Noise Ratio of Scoring Functions for Protein−Ligand Docking

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