Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin

Sridhar, Akshay; Ross, Gregory A.; Biggin, Philip C.

doi:10.1371/journal.pone.0172743

Cited by 39 publications

(42 citation statements)

References 67 publications

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“…Hence, the correct assignment of disconnected electron densities in crystal structures forms the basis for deriving functional mechanisms from structural data. However, the assignment of densities is in many cases far from obvious [ 9 – 17 , 84 , 85 ].…”

Section: Discussionmentioning

confidence: 99%

Assigning crystallographic electron densities with free energy calculations—The case of the fluoride channel Fluc

Ariz-Extreme

Hub

2018

PLoS ONE

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Approximately 90% of the structures in the Protein Data Bank (PDB) were obtained by X-ray crystallography or electron microscopy. Whereas the overall quality of structure is considered high, thanks to a wide range of tools for structure validation, uncertainties may arise from density maps of small molecules, such as organic ligands, ions or water, which are non-covalently bound to the biomolecules. Even with some experience and chemical intuition, the assignment of such disconnected electron densities is often far from obvious. In this study, we suggest the use of molecular dynamics (MD) simulations and free energy calculations, which are well-established computational methods, to aid in the assignment of ambiguous disconnected electron densities. Specifically, estimates of (i) relative binding affinities, for instance between an ion and water, (ii) absolute binding free energies, i.e., free energies for transferring a solute from bulk solvent to a binding site, and (iii) stability assessments during equilibrium simulations may reveal the most plausible assignments. We illustrate this strategy using the crystal structure of the fluoride specific channel (Fluc), which contains five disconnected electron densities previously interpreted as four fluoride and one sodium ion. The simulations support the assignment of the sodium ion. In contrast, calculations of relative and absolute binding free energies as well as stability assessments during free MD simulations suggest that four of the densities represent water molecules instead of fluoride. The assignment of water is compatible with the loss of these densities in the non-conductive F82I/F85I mutant of Fluc. We critically discuss the role of the ion force fields for the calculations presented here. Overall, these findings indicate that MD simulations and free energy calculations are helpful tools for modeling water and ions into crystallographic density maps.

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Section: Discussionmentioning

confidence: 99%

Assigning crystallographic electron densities with free energy calculations—The case of the fluoride channel Fluc

Ariz-Extreme

Hub

2018

PLoS ONE

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“…The binding site of a protein is flexible and can adopt diverse conformational states, generally at the level of side chain residues but often also involving structural changes in loops and the remodeling of secondary structural elements induced upon ligand binding [ 42 , 43 , 44 , 45 ]. Furthermore, the outcome of docking studies may be largely affected by the identification of water molecules that mediate the interactions of the ligand in the binding pocket, making it necessary to explore the potential role of bridging waters or networks of ordered waters in docking calculations [ 46 , 47 , 48 , 49 , 50 ]. On the other hand, providing an accurate score and even estimating the binding affinity at a reasonable cost compatible with the screening of large chemical libraries is still challenging for docking methods [ 51 , 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

135

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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

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“…Thus, at present it seems wise to pay attention to the starting conditions of the free energy calculations to maximize cost effectiveness. Where experimental data is lacking, a number of molecular modelling protocols have been proposed to determine location and energetics of important binding site water molecules [58][59][60][61][62][63][64][65][66][67].…”

Section: Discussionmentioning

confidence: 99%

Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

Granadino‐Roldán

Mey

Pérez

et al. 2018

Preprint

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Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking – MM/PBSA – AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.

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Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin

Cited by 39 publications

References 67 publications

Assigning crystallographic electron densities with free energy calculations—The case of the fluoride channel Fluc

Assigning crystallographic electron densities with free energy calculations—The case of the fluoride channel Fluc

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

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