Efficient molecular surface generation using level-set methods

Can, Tolga; Chen, Chao-I; Wang, Yuan-Fang

doi:10.1016/j.jmgm.2006.02.012

Cited by 63 publications

(78 citation statements)

References 17 publications

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“…We have compared the exact SES surface using chimera for a molecule of 3,967 atoms (this computation is not practical for very large molecules), and the result of our algorithm using a resolution of 256 3 . To this end, we sampled 2,676,613 random points uniformly distributed over the surface given by our algorithm, and computed their distance to Table 3 Performance comparison between our method and the EDTSurf software [28].…”

Section: Resultsmentioning

confidence: 99%

“…Instead, we chose a more GPUfriendly solution. We grouped the grid points into bricks of 8 3 , and then, we selected those that need further refinement. These bricks are defined as the ones that have at least one boundary point (see the yellow bricks in Figure 7).…”

Section: Methodsmentioning

confidence: 99%

“…While the resulting surfaces are less accurate than the analytical descriptions, these algorithms have the advantage that they are are fast since the computations are less involved, and that they are relatively simple to implement. Can et al [3] developed a method based on level sets. Yu [29] presented an efficient algorithm using lists to speed up the computations.…”

Section: Previous Workmentioning

confidence: 99%

“…If two points satisfy these conditions, a probe might fit between them. For these bricks, our algorithm is executed with a resolution of 64 3 . If a probe can be placed in one of these new sample points, our mipmap is updated with the newly calculated distances at the highest resolution.…”

Section: Detection Of Missing Featuresmentioning

confidence: 99%

“…We chose to encode these properties at the lower resolution to have smooth transitions between colors, since higher resolutions would make the borders between colors clearly visible. Moreover, it does not add extra computational costs -it can be computed once together with the coarsest level-and it uses limited extra memory, as the selected resolution is never greater than 128 3 . Different examples are presented in Figure 10.…”

Section: Ses Coloringmentioning

confidence: 99%

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Interactive GPU-based generation of solvent-excluded surfaces

et al. 2017

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The Solvent Excluded Surface (SES) is a popular molecular representation that gives the boundary of the molecular volume with respect to a specific solvent. SESs depict which areas of a molecule are accessible by a specific solvent, which is represented as a spherical probe. Despite the popularity of SESs, their generation is still a computeintensive process, which is often performed in a pre-processing stage prior to the actual rendering (except for small models). For dynamic data or varying probe radii, however, such a pre-processing is not feasible as it prevents interactive visual analysis. Thus, we present a novel approach for the on-the-fly generation of SESs, a highly parallelizable, grid-based algorithm where the SES is rendered using ray-marching. By exploiting modern GPUs, we are able to rapidly generate SESs directly within the mapping stage of the visualization pipeline. Our algorithm can be applied to large time-varying molecules and is scalable, as it can progressively refine the SES if GPU capabilities are insufficient. In this paper, we show how our algorithm is realized and how smooth transitions are achieved during progressive refinement. We further show visual results obtained from real world data, and discuss the performance obtained, which improves upon previous techniques in both the size of the molecules that can be handled and the resulting frame rate.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Detection Of Missing Featuresmentioning

confidence: 99%

Section: Ses Coloringmentioning

confidence: 99%

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Interactive GPU-based generation of solvent-excluded surfaces

et al. 2017

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Using polarizable POSSIM force field and fuzzy‐border continuum solvent model to calculate pK_a shifts of protein residues

Sharma

Kaminski

2016

J Comput Chem

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Our Fuzzy-Border (FB) continuum solvent model has been extended and modified to produce hydration parameters for small molecules using POlarizable Simulations Second-order Interaction Model (POSSIM) framework with an average error of 0.136 kcal/mol. It was then used to compute pKa shifts for carboxylic and basic residues of the turkey ovomucoid third domain (OMTKY3) protein. The average unsigned errors in the acid and base pKa values were 0.37 and 0.4 pH units, respectively, versus 0.58 and 0.7 pH units as calculated with a previous version of polarizable protein force field and Poisson Boltzmann continuum solvent. This POSSIM/FB result is produced with explicit refitting of the hydration parameters to the pKa values of the carboxylic and basic residues of the OMTKY3 protein, thus the values of the acidity constants can be viewed as additional fitting target data. In addition to calculating pKa shifts for the OMTKY3 residues, we have studied aspartic acid residues of Rnase Sa. This was done without any further refitting of the parameters and agreement with the experimental pKa values is within an average unsigned error of 0.65 pH units. This result included the Asp79 residue that is buried and thus has a high experimental pKa value of 7.37 units. Thus, the presented model is capable or reproducing pKa results for residues in an environment that is significantly different from the solvated protein surface used in the fitting. Therefore, the POSSIM force field and the Fuzzy Border continuum solvent parameters have been demonstrated to be sufficiently robust and transferable.

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Comparison of the MSMS and NanoShaper molecular surface triangulation codes in the TABI Poisson–Boltzmann solver

Wilson

Krasny

2021

J Comput Chem

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The Poisson-Boltzmann (PB) implicit solvent model is a popular framework for studying the electrostatics of solvated biomolecules. In this model the dielectric interface between the biomolecule and solvent is often taken to be the molecular surface or solvent-excluded surface (SES), and the quality of the SES triangulation is critical in boundary element simulations of the model. This work compares the performance of the MSMS and NanoShaper surface triangulation codes for a set of 38 biomolecules.While MSMS produces triangles of exceedingly small area and large aspect ratio, the two codes yield comparable values for the SES surface area and electrostatic solvation energy, where the latter calculations were performed using the treecodeaccelerated boundary integral (TABI) PB solver. However we found that NanoShaper is computationally more efficient and reliable than MSMS, especially when parameters are set to produce highly resolved triangulations.

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Efficient molecular surface generation using level-set methods

Cited by 63 publications

References 17 publications

Interactive GPU-based generation of solvent-excluded surfaces

Interactive GPU-based generation of solvent-excluded surfaces

Using polarizable POSSIM force field and fuzzy‐border continuum solvent model to calculate pK_a shifts of protein residues

Comparison of the MSMS and NanoShaper molecular surface triangulation codes in the TABI Poisson–Boltzmann solver

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Efficient molecular surface generation using level-set methods

Cited by 63 publications

References 17 publications

Interactive GPU-based generation of solvent-excluded surfaces

Interactive GPU-based generation of solvent-excluded surfaces

Using polarizable POSSIM force field and fuzzy‐border continuum solvent model to calculate pKa shifts of protein residues

Comparison of the MSMS and NanoShaper molecular surface triangulation codes in the TABI Poisson–Boltzmann solver

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Using polarizable POSSIM force field and fuzzy‐border continuum solvent model to calculate pK_a shifts of protein residues