蛋白质序列设计有着广泛应用前景。如何设计热力学、动力学特性以及序列多样性与天然蛋白类似的人工序列，是并未解决的问题。目前，相关方法发展的主要障碍之一，是缺乏对序列可折叠性进行实验测定的高效方法。对设计序列是否可折叠只能逐条检验，成本高，效率低，有限的实验结果很难提示如何改进理论方法。本课题主要研究对序列可折叠性进行检测的高效实验方法：拟采用基于Split β-内酰胺酶将蛋白质折叠稳定性与宿主的抗药性关联的系统，将其应用于对理论设计序列，特别是对根据统计势能函数设计的序列的可折叠性进行系统分析；还拟利用该实验体系的高效筛选能力对设计序列的折叠能力进行实验改进和优化，从而为改进设计方法提供可靠的实验反馈和指导。

The De novo rational protein design occupies an exciting field in structural biology with increasing importance and huge application potentials in industrial and biomedical engineering. On the other hand, high-throughput approaches evaluating foldability of artificial designed proteins remain appealing and challenging in protein design field. Here we describe a new designing platform combining more accurate computational prediction and high-throughput approaches in designing and optimizing artificial proteins. We would utilize statistical potential to overcome the limitation of existed software while design several proteins with varies of conformations. It is an energy function derived from an analysis of known protein structures in the PDB database and previously we proved that using this potential readily improved the accuracy of artificial protein structural prediction. On the other hand, a high throughput endued split β-lactamase system which directly links the in vivo foldability of proteins to antibiotic resistance would be constructed, it's feasibility as well as parameter optimization will be studied. We would apply this system to the quantitative evaluation towards the folding of the designed proteins, and also conduct directed evolution to look for folding stabilized candidates from a high throughput library based on the wild type protein sequence. The structures of these stabilized mutants would be studied by NMR spectroscopy, as the results would serve to validate our computational prediction. Meanwhile, all experimental data (sequence, in vivo folding ability, structures, etc) would help optimize the designing process. Our long term goal is to build a generalized, theoretical and experimental approaches combined de novo protein designing platform, which would bring significant innovations to the artificial protein engineering study.