{"title":"MEGADOCK 3.0:一个高性能的蛋白质-蛋白质相互作用预测软件,使用混合并行计算用于千万亿次超级计算环境。","authors":"Yuri Matsuzaki, Nobuyuki Uchikoga, Masahito Ohue, Takehiro Shimoda, Toshiyuki Sato, Takashi Ishida, Yutaka Akiyama","doi":"10.1186/1751-0473-8-18","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.</p><p><strong>Results: </strong>We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, \"MEGADOCK\", by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.</p><p><strong>Conclusions: </strong>We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"18"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-18","citationCount":"28","resultStr":"{\"title\":\"MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments.\",\"authors\":\"Yuri Matsuzaki, Nobuyuki Uchikoga, Masahito Ohue, Takehiro Shimoda, Toshiyuki Sato, Takashi Ishida, Yutaka Akiyama\",\"doi\":\"10.1186/1751-0473-8-18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.</p><p><strong>Results: </strong>We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, \\\"MEGADOCK\\\", by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.</p><p><strong>Conclusions: </strong>We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.</p>\",\"PeriodicalId\":35052,\"journal\":{\"name\":\"Source Code for Biology and Medicine\",\"volume\":\"8 1\",\"pages\":\"18\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/1751-0473-8-18\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Source Code for Biology and Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/1751-0473-8-18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Source Code for Biology and Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1751-0473-8-18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Decision Sciences","Score":null,"Total":0}
MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments.
Background: Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.
Results: We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, "MEGADOCK", by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.
Conclusions: We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.
期刊介绍:
Source Code for Biology and Medicine is a peer-reviewed open access, online journal that publishes articles on source code employed over a wide range of applications in biology and medicine. The journal"s aim is to publish source code for distribution and use in the public domain in order to advance biological and medical research. Through this dissemination, it may be possible to shorten the time required for solving certain computational problems for which there is limited source code availability or resources.