A challenging system: Free energy prediction for factor Xa

Wallnoefer, Hannes G.; Liedl, Klaus R.; Fox, Thomas

doi:10.1002/jcc.21758

Cited by 74 publications

(90 citation statements)

References 68 publications

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“…The proteins were equilibrated as described earlier (Wallnoefer, Liedl, & Fox, 2011; Wallnoefer, Lingott, Gutierrez, Merfort, & Liedl, 2010), applying the Sander module of Amber10 (Case et al, 2008). Hydrogen positions were optimized (500 steps steepest descent, 500 steps conjugate gradient) with position restraints on heavy atoms (1000 kcal/mol Å 2 ).…”

Section: Methodsmentioning

confidence: 99%

Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity

Grafenstein

Wallnoefer

Kirchmair

et al. 2013

Journal of Biomolecular Structure and Dynamics

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Influenza virus neuraminidase (iNA) is a homotetrameric surface protein of the influenza virus and an established target for antiviral drugs. In contrast to neuraminidases (NAs) of other biological systems (non-iNAs), enzymatic activity of iNA is only observed in a quaternary assembly and iNA needs the tetramerization to mediate enzymatic activity. Obviously, differences on a molecular level between iNA and non-iNAs are responsible for this intriguing observation. Comparison between protein structures and multiple sequence alignment allow the identification of differences in amino acid composition in crucial regions of the enzyme, such as next to the conserved D151 and the 150-loop. These differences in amino acid sequence and protein tetramerization are likely to alter the dynamics of the system. Therefore, we performed molecular dynamics simulations to investigate differences in the molecular flexibility of monomers, dimers, and tetramers of iNAs of subtype N1 (avian 2004, pandemic 1918 and pandemic 2009 iNA) and as comparison the non-iNA monomer from Clostridium perfringens. We show that conformational transitions of iNA are crucially influenced by its assembly state. The protein–protein interface induces a complex hydrogen-bonding network between the 110-helix and the 150-loop, which consequently stabilizes the structural arrangement of the binding site. Therefore, we claim that these altered dynamics are responsible for the dependence of iNA’s catalytic activity on the tetrameric assembly. Only the tetramerization-induced balance between stabilization and altered local flexibility in the binding site provides the appropriate arrangement of key residues for iNA’s catalytic activity.

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Section: Methodsmentioning

confidence: 99%

Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity

Grafenstein

Wallnoefer

Kirchmair

et al. 2013

Journal of Biomolecular Structure and Dynamics

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“…The careful consideration of hydration sites has been shown to aid the predictability of 3D QSAR models, [9]–[11] ensure stable simulations with molecular dynamics [12], and improve the accuracy of rigorous free energy calculations [13]. Continuum solvent models have also been reported to improve with the addition of explicit water molecules [14]. Traditionally, ordered water molecules were ignored in ligand docking studies and ligands were docked into desolvated binding sites.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Morris²,

2012

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Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

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“…In variance to previous attempts with the same aim [30,31,32,33,34,35,36,37,38], we did not use a fixed number of water molecules, but instead included all water molecules in the binding site, which varied in the different snapshots. Therefore, we grouped the snapshots after the number of water molecules and treated them separately, obtaining the final affinity as a weighted mean over the various numbers of water molecules in the binding site (Eqn.…”

Section: Discussionmentioning

confidence: 99%

“…This has been done before [30,31,32,33,34,35,36,37,38] and it is simple to implement. You only have to identify the water molecules of interest and change the scripts so that they are considered as a part of the receptor.…”

Section: Mm/gbsa With Explicit Water Moleculesmentioning

confidence: 99%

“…In several cases, explicit water molecules have been included in MM/GBSA calculations as part of the receptor, often giving improved results [30,31,32,33,34,35,36,37,38]. For example, a study of the binding of six inhibitors to blood-clotting factor Xa showed that the MM/PBSA predictions were strongly improved if one water molecule that mediated the interaction between some of the ligands and the protein back-bone was explicitly considered (the correlation coefficient, r, increased from -0.8 to +0.8) [37]. Similar results were obtained if all water molecules that remained in a single position during the MD simulations were treated as a part of the protein.…”

Section: Introductionmentioning

confidence: 99%

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Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach

2014

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General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal AbstractWe have tested different approaches to include the effect of binding-site water molecules for ligand-binding affinities within the MM/GBSA approach (molecular mechanics combined with generalised Born and surface-area solvation). As a test case, we study the binding of nine phenol analogues to ferritin. The effect of water molecules mediating the interaction between the receptor and the ligand can be studied by considering a few water molecules as a part of the receptor. We extend previous methods by allowing for a variable number of water molecules in the binding site. The effect of displaced water molecules can also be considered within the MM/GBSA philosophy by calculating the affinities of binding-site water molecules, both before and after the binding of the ligand. To obtain proper energies, both the water molecules and the ligand need then to be converted to non-interacting ghost molecules and a single-average approach (i.e. the same structures are used for bound and unbound states) based on the simulations of both the complex and the free receptor can be used to improve the precision. The only problem is to estimate the free energy of an unbound water molecule. With an experimental estimate of this parameter, promising results are obtained for our test case.Key Words: ligand-binding affinities, MM/GBSA, water displacement, water-mediated binding, molecular dynamics, entropy. 2 IntroductionOne of the largest challenges for computational chemistry is to develop methods to calculate the binding free energy (Gbind) of a ligand (L, e.g. a drug candidate) to its receptor (R, e.g. a protein or nucleic acid), forming a complex (RL).

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A challenging system: Free energy prediction for factor Xa

Cited by 74 publications

References 68 publications

Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity

Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach

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