TY - JOUR
T1 - High throughput computing to improve efficiency of predicting protein stability change upon mutation
AU - Wu, Chao Chin
AU - Lai, Lien Fu
AU - Gromiha, M. Michael
AU - Huang, Liang Tsung
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84905569262&partnerID=8YFLogxK
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U2 - 10.1504/IJDMB.2014.064011
DO - 10.1504/IJDMB.2014.064011
M3 - Article
C2 - 25796739
AN - SCOPUS:84905569262
VL - 10
SP - 206
EP - 224
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
SN - 1748-5673
IS - 2
ER -