High throughput computing to improve efficiency of predicting protein stability change upon mutation

Chao Chin Wu, Lien Fu Lai, M. Michael Gromiha, Liang Tsung Huang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Predicting protein stability change upon mutation is important for protein design. Although several methods have been proposed to improve prediction accuracy it will be difficult to employ those methods when the required input information is incomplete. In this work, we integrated a fuzzy query model based on the knowledge-based approach to overcome this problem, and then we proposed a high throughput computing method based on parallel technologies in emerging cluster or grid systems to discriminate stability change. To improve the load balance of heterogeneous computing power in cluster and grid nodes, a variety of self-scheduling schemes have been implemented. Further, we have tested the method by performing different analyses and the results showed that the present method can process hundreds of predication queries in more reasonable response time and perform a super linear speedup to a maximum of 86.2 times. We have also established a website tool to implement the proposed method and it is available at http:// bioinformatics.myweb.hinet.net/para.htm.

Original languageEnglish
Pages (from-to)206-224
Number of pages19
JournalInternational Journal of Data Mining and Bioinformatics
Volume10
Issue number2
DOIs
Publication statusPublished - 2014

Fingerprint

Protein Stability
Throughput
Proteins
efficiency
Mutation
Bioinformatics
Websites
Scheduling
Computational Biology
Reaction Time
scheduling
website
Technology
knowledge

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

Cite this

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High throughput computing to improve efficiency of predicting protein stability change upon mutation. / Wu, Chao Chin; Lai, Lien Fu; Gromiha, M. Michael; Huang, Liang Tsung.

In: International Journal of Data Mining and Bioinformatics, Vol. 10, No. 2, 2014, p. 206-224.

Research output: Contribution to journalArticle

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