Parallel, stochastic measurement of molecular surface area
Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorith...
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| Veröffentlicht in: | Journal of molecular graphics & modelling Jg. 27; H. 1; S. 82 - 87 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.08.2008
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| ISSN: | 1093-3263, 1873-4243, 1873-4243 |
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| Abstract | Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited.
We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering.
We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy. |
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| AbstractList | Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited.
We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering.
We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy. Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy. Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy.Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited. We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering. We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy. |
| Author | Juba, Derek Varshney, Amitabh |
| Author_xml | – sequence: 1 givenname: Derek surname: Juba fullname: Juba, Derek email: juba@cs.umd.edu – sequence: 2 givenname: Amitabh surname: Varshney fullname: Varshney, Amitabh email: varshney@cs.umd.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18424205$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_jmgm_2010_06_010 crossref_primary_10_1186_1471_2105_14_S4_S4 crossref_primary_10_1016_j_jcp_2010_01_004 crossref_primary_10_1088_1749_4699_6_1_015007 crossref_primary_10_1007_s10509_018_3265_6 crossref_primary_10_1371_journal_pone_0059744 crossref_primary_10_1016_j_patcog_2010_06_002 crossref_primary_10_1080_07391102_2010_10507359 crossref_primary_10_1007_s00244_015_0236_6 |
| Cites_doi | 10.1016/j.cad.2005.07.002 10.1126/science.6879170 10.1145/146382.146385 10.1107/S0021889883010985 10.1016/j.jmb.2006.05.023 10.1073/pnas.77.4.1736 10.1109/TVCG.2007.70517 10.1016/j.jmgm.2006.02.012 10.1145/357306.357310 10.1145/1185657.1185660 10.1146/annurev.bb.06.060177.001055 10.1002/(SICI)1097-0134(19981001)33:1<18::AID-PROT2>3.0.CO;2-H 10.1145/174462.156635 10.1109/38.310720 10.1002/prot.10379 10.1016/0022-2836(71)90324-X 10.1016/S0010-4485(02)00100-8 10.1002/(SICI)1096-987X(199802)19:3<319::AID-JCC6>3.0.CO;2-W 10.1021/j100011a016 10.1093/nar/28.1.235 |
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| SubjectTerms | Algorithms Databases, Protein GPU Models, Molecular Molecular surface Parallel Progressive Proteins - chemistry Quasi-random Stochastic Stochastic Processes Surface Properties Time Factors |
| Title | Parallel, stochastic measurement of molecular surface area |
| URI | https://dx.doi.org/10.1016/j.jmgm.2008.03.001 https://www.ncbi.nlm.nih.gov/pubmed/18424205 https://www.proquest.com/docview/69465510 |
| Volume | 27 |
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