Pomelo, a tool for computing Generic Set Voronoi Diagrams of Aspherical Particles of Arbitrary Shape

Weis, Simon; Schönhöfer, Philipp W. A.; Schaller, Fabian M.; Schröter, Matthias; Schröder‐Turk, Gerd E.

doi:10.1051/epjconf/201714006007

Cited by 24 publications

(19 citation statements)

References 11 publications

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“…To calculate the Voronoi tessellation for a protein structure, we use the surface Voronoi tessellation, which defines a Voronoi cell as the region of space in a given system that is closer to the bounding surface of the residue than to the bounding surface of any other residue in the system. We calculate the surface Voronoi tessellations using the Pomelo software package 76 . This software approximates the bounding surfaces of each residue by triangulating points on the residue surfaces.…”

Section: Methodsmentioning

confidence: 99%

“…We calculate the surface Voronoi tessellations using the Pomelo software package. 76 This software approximates the bounding surfaces of each residue by triangulating points on the residue surfaces. We find that using 400 points per atom, or 6, 400 surface points per residue, gives an accurate representation of the Voronoi cells and the results do not change if more surface points are included.…”

Section: Packing Fractionmentioning

confidence: 99%

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Using physical features of protein core packing to distinguish real proteins from decoys

et al. 2020

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The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user‐specified global root‐mean‐squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed‐forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state‐of‐the‐art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Packing Fractionmentioning

confidence: 99%

Using physical features of protein core packing to distinguish real proteins from decoys

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To calculate the Voronoi tessellation for a protein structure, we use the surface Voronoi tessellation, which defines a Voronoi cell as the region of space in a given system that is closer to the bounding surface of the residue than to the bounding surface of any other residue in the system. We calculate the surface Voronoi tessellations using the Pomelo software package [62]. This software approximates the bounding surfaces of each residue by triangulating points on the residue surfaces.…”

Section: Packing Fractionmentioning

confidence: 99%

Using physical features of protein core packing to distinguish real proteins from decoys

Grigas¹,

Mei²,

Treado³

et al. 2020

Preprint

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The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (specifically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features.

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“…To calculate the Voronoi tessellation for a given protein core, we employ surface Voronoi tessellation [50], which defines a Voronoi cell as the region of space that is closer to the bounding surface of residue µ than to the bounding surface of any other residue in the protein. We calculate the surface Voronoi tessellation using POMELO software package [51]. This software approximates the bounding surfaces of each residue by triangulating points on the residue surfaces.…”

Section: Packing Fractionmentioning

confidence: 99%

Current MD forcefields fail to capture key features of protein structure and fluctuations: A case study of cyclophilin A and T4 lysozyme

Mei,

Grigas,

Treado

et al. 2020

Preprint

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Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores.

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Pomelo, a tool for computing Generic Set Voronoi Diagrams of Aspherical Particles of Arbitrary Shape

Cited by 24 publications

References 11 publications

Using physical features of protein core packing to distinguish real proteins from decoys

Using physical features of protein core packing to distinguish real proteins from decoys

Using physical features of protein core packing to distinguish real proteins from decoys

Current MD forcefields fail to capture key features of protein structure and fluctuations: A case study of cyclophilin A and T4 lysozyme

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