GADIS: Algorithm for designing sequences to achieve target secondary structure profiles of intrinsically disordered proteins
GADIS: Algorithm for designing sequences to achieve target secondary structure profiles of intrinsically disordered proteins
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DOI：
10.1093/protein/gzw034
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发表时间：
2016-09-01
影响因子：
2.4
通讯作者：
Pappu, Rohit V.
中科院分区：
文献类型：
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作者：
Harmon, Tyler S.;Crabtree, Michael D.;Pappu, Rohit V.
许多内在无序蛋白（IDP）参与耦合折叠和结合反应，并在其结合复合物中形成α螺旋结构。丙氨酸、甘氨酸或脯氨酸扫描诱变方法通常用于剖析内在螺旋度对偶联折叠和结合的贡献。这些实验可能产生混淆的结果，因为诱变策略改变了IDP的氨基酸组成。因此，一个重要的下一步诱变为基础的方法，耦合折叠和结合的机制研究是设计的序列，满足三个主要的限制。这些是（i）实现目标固有α螺旋度分布;（ii）固定对应于结合界面的残基的位置;和（iii）保持天然氨基酸组成。在这里，我们报告的发展，遗传算法设计的内在二级结构（GADIS）的设计序列，满足指定的约束条件。我们描述的算法和目前的结果，以证明GADIS的适用性，通过设计的序列变体的本质上无序的MANTA系统，经历耦合折叠和结合到Mcl-1。我们的序列设计涵盖了一系列的内在螺旋度分布。序列编码的平均螺旋度的预测变化进行测试对实验测量。
Many intrinsically disordered proteins (IDPs) participate in coupled folding and binding reactions and form alpha helical structures in their bound complexes. Alanine, glycine, or proline scanning mutagenesis approaches are often used to dissect the contributions of intrinsic helicities to coupled folding and binding. These experiments can yield confounding results because the mutagenesis strategy changes the amino acid compositions of IDPs. Therefore, an important next step in mutagenesis-based approaches to mechanistic studies of coupled folding and binding is the design of sequences that satisfy three major constraints. These are (i) achieving a target intrinsic alpha helicity profile; (ii) fixing the positions of residues corresponding to the binding interface; and (iii) maintaining the native amino acid composition. Here, we report the development of a Genetic Algorithm for Design of Intrinsic secondary Structure (GADIS) for designing sequences that satisfy the specified constraints. We describe the algorithm and present results to demonstrate the applicability of GADIS by designing sequence variants of the intrinsically disordered PUMA system that undergoes coupled folding and binding to Mcl-1. Our sequence designs span a range of intrinsic helicity profiles. The predicted variations in sequence-encoded mean helicities are tested against experimental measurements.