Protein Design Based on Parallel Dimensional Reduction

The design of proteins with targeted properties is a computationally intensive task with large memory requirements. We have developed a novel approach that combines a dimensional reduction of the problem with a High Performance Computing platform to efficiently design large proteins. This tool overcomes the memory limits of the process, allowing the design of proteins whose requirements prevent them to be designed in traditional sequential platforms. We have applied our algorithm to the design of functional proteins, optimizing for both catalysis and stability. We have also studied the redesign of dimerization interfaces, taking simultaneously into account the stability of the subunits of the dimer. However, our methodology can be applied to any computational chemistry application requiring combinatorial optimization techniques.具备靶向特性的蛋白质设计是一项计算密集型且内存需求极高的任务。我们开发了一种全新方法，将该问题的降维处理与高性能计算（High Performance Computing）平台相结合，以高效完成大型蛋白质的设计工作。该工具突破了流程中的内存限制，可实现那些因性能约束而无法在传统串行平台上完成设计的蛋白质的构建。我们已将所提出的算法应用于功能蛋白质的设计，同时针对催化活性与结构稳定性进行多目标优化。此外，我们还针对二聚化界面的重新设计开展了研究，同时兼顾二聚体各亚基的结构稳定性。不过，我们的方法学可推广至任何需要组合优化技术的计算化学应用场景中。