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Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials

Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic...

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Autores principales: Georgiev, Georgi D., Dodd, Kevin F., Chen, Brian Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247173/
https://www.ncbi.nlm.nih.gov/pubmed/32489400
http://dx.doi.org/10.1186/s13015-020-00168-z
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author Georgiev, Georgi D.
Dodd, Kevin F.
Chen, Brian Y.
author_facet Georgiev, Georgi D.
Dodd, Kevin F.
Chen, Brian Y.
author_sort Georgiev, Georgi D.
collection PubMed
description Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic efficiency and geometric precision are central to algorithmic design. To address these concerns, this paper presents pClay, the first algorithm to perform parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials as geometric solids. pClay was presented at the 2019 Workshop on Algorithms in Bioinformatics (WABI 2019) and is described in expanded detail here, especially with regard to the comparison of electrostatic isopotentials. Earlier methods have generally used parallelism to enhance computational throughput, pClay is the first algorithm to use parallelism to make arbitrarily high precision comparisons practical. It is also the first method to demonstrate that high precision comparisons of geometric solids can yield more precise structural inferences than algorithms that use existing standards of precision. One advantage of added precision is that statistical models can be trained with more accurate data. Using structural data from an existing method, a model of steric variations between binding cavities can overlook 53% of authentic steric influences on specificity, whereas a model trained with data from pClay overlooks none. Our results also demonstrate the parallel performance of pClay on both workstation CPUs and a 61-core Xeon Phi. While slower on one core, additional processor cores rapidly outpaced single core performance and existing methods. Based on these results, it is clear that pClay has applications in the automatic explanation of binding mechanisms and in the rational design of protein binding preferences.
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spelling pubmed-72471732020-06-01 Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials Georgiev, Georgi D. Dodd, Kevin F. Chen, Brian Y. Algorithms Mol Biol Research Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic efficiency and geometric precision are central to algorithmic design. To address these concerns, this paper presents pClay, the first algorithm to perform parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials as geometric solids. pClay was presented at the 2019 Workshop on Algorithms in Bioinformatics (WABI 2019) and is described in expanded detail here, especially with regard to the comparison of electrostatic isopotentials. Earlier methods have generally used parallelism to enhance computational throughput, pClay is the first algorithm to use parallelism to make arbitrarily high precision comparisons practical. It is also the first method to demonstrate that high precision comparisons of geometric solids can yield more precise structural inferences than algorithms that use existing standards of precision. One advantage of added precision is that statistical models can be trained with more accurate data. Using structural data from an existing method, a model of steric variations between binding cavities can overlook 53% of authentic steric influences on specificity, whereas a model trained with data from pClay overlooks none. Our results also demonstrate the parallel performance of pClay on both workstation CPUs and a 61-core Xeon Phi. While slower on one core, additional processor cores rapidly outpaced single core performance and existing methods. Based on these results, it is clear that pClay has applications in the automatic explanation of binding mechanisms and in the rational design of protein binding preferences. BioMed Central 2020-05-25 /pmc/articles/PMC7247173/ /pubmed/32489400 http://dx.doi.org/10.1186/s13015-020-00168-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Georgiev, Georgi D.
Dodd, Kevin F.
Chen, Brian Y.
Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title_full Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title_fullStr Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title_full_unstemmed Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title_short Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
title_sort precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247173/
https://www.ncbi.nlm.nih.gov/pubmed/32489400
http://dx.doi.org/10.1186/s13015-020-00168-z
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