Department of Electrical and Computer Engineering and TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, USA.
Bioinformatics. 2018 Sep 1;34(17):i811-i820. doi: 10.1093/bioinformatics/bty564.
Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design.
We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity.
https://shen-lab.github.io/software/iCFN.
Supplementary data are available at Bioinformatics online.
多态蛋白设计通过考虑每个亚态的正负蛋白状态及其子集中的亚态，解决了多特异性设计和骨架灵活性等实际问题。但它也对保证最优解的精确算法提出了巨大挑战，从而无法直接检验模型背后的机制假设。然而，多态蛋白设计中缺乏有效的精确算法。
我们开发了一种名为互联代价函数网络（iCFN）的高效精确算法，用于多态蛋白设计。其通用的公式允许广泛的应用，如稳定性、亲和力和特异性设计，同时解决了蛋白质骨架全局灵活性等问题。iCFN 将每个亚态设计视为通过 CFN 建模的加权约束满足问题（WCSP）；并通过新颖的界和基于树结构的序列、亚态和构象的深度优先分支和边界搜索来解决耦合的 WCSP。当 iCFN 应用于 T 细胞受体的特异性设计时，它大大减少了搜索空间和运行时间，使问题变得可行，这是精确方法前所未有的规模问题。此外，iCFN 生成的受体设计与最先进的方法相比，具有更好的准确性，这突显了在蛋白质设计中建模骨架灵活性的重要性，并揭示了结合特异性的分子机制。
https://shen-lab.github.io/software/iCFN。
补充数据可在生物信息学在线获得。
