CAJ | 학술논문

蛋白质识别关键区域的研究对揭示生命现象的本质规律,提高药物设计效率,降低新药物开发的成本和周期有重大的应用价值.但由于蛋白质大分子结构的高度复杂性,一般的计算机系统难以对蛋白质识别过程中结构与功能的连续性变化实现快速动态分析.设计并实现了一种基于GPU/CPU异构的集群系统,根据生物计算的特点对异构集群进行数据结构和算法设计,建立起基于GPU的Kd-tree构造和访问的高效算法,以提高系统并行计算的性能.在此基础上对蛋白质分子场的动态时变序列进行快速计算,对结果进行分类,能快速高效地确定出蛋白质的相互作用关键区域.该方法得到了相应的生化实验结果验证,为揭示蛋白质作用机制提供了一种高效直观的新方法.
단백질식별관건구역적연구대게시생명현상적본질규률,제고약물설계효솔,강저신약물개발적성본화주기유중대적응용개치.단유우단백질대분자결구적고도복잡성,일반적계산궤계통난이대단백질식별과정중결구여공능적련속성변화실현쾌속동태분석.설계병실현료일충기우GPU/CPU이구적집군계통,근거생물계산적특점대이구집군진행수거결구화산법설계,건립기기우GPU적Kd-tree구조화방문적고효산법,이제고계통병행계산적성능.재차기출상대단백질분자장적동태시변서렬진행쾌속계산,대결과진행분류,능쾌속고효지학정출단백질적상호작용관건구역.해방법득도료상응적생화실험결과험증,위게시단백질작용궤제제공료일충고효직관적신방법.
It has been found that key area research during protein recognition process is typically important for proclaiming biological phenomena essence, improving drug design efficiency and decreasing cost and shortening industrial cycle of new drug design. For high complexity of protein macromolecule structure, common computer systems are not capable for continuously tracking coherence change of structure and function during protein recognition. This paper sets up a heterogeneous cluster based on GPU/CPU as a uniform biological computing environment, proposes particular data structures and algorithms to deal with large biological data, and reconfigures Kd-tree based on GPU and data access modes to improve parallel calculation performance. Thus it becomes feasible to calculate time-varying protein molecular filed fast enough to do further analysis on key area for protein recognition. The corresponding biochemistry experiment results show that the method can be used as a new effective and intuitive tool for protein interaction research.