title: High throughput computing to improve efficiency of predicting protein stability change upon mutation.
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.
authors: Chao-Chin
authors: CC
authors: Wu
authors: Lien-Fu
authors: LF
authors: Lai
authors: M Michael
authors: MM
authors: Gromiha
authors: Liang-Tsung
authors: LT
authors: Huang
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution / Q000235:genetics*; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D004355:Drug Stability; D017143:Fuzzy Logic; D057166:High-Throughput Screening Assays / Q000379:methods*; D008956:Models, Chemical; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*
chemical_list: D011506:Proteins