Protein design automation
Protein design automation
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DOI：
10.1002/pro.5560050511
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发表时间：
1996-05-01
期刊：
影响因子：
8
通讯作者：
Mayo, SL
中科院分区：
文献类型：
--
作者：
Dahiyat, BI;Mayo, SL
我们已经构思并实施了一种周期性的蛋白质设计策略，该策略将理论，计算和实验测试融合在一起。在组合上，大量可能的序列以及对控制蛋白质结构的因素的不完全理解是蛋白质设计中的主要障碍。我们的蛋白质设计自动化算法客观地预测蛋白质序列可能达到所需的折叠。使用侧链的Rotamer描述，我们基于死端消除定理实现了快速离散的搜索算法，以迅速从大量可能的解决方案中迅速找到其最佳几何形状中的全球最佳序列。使用范德华电位对旋转序列进行了旋转序列的空间互补性。然后以最佳序列从最佳序列执行蒙特卡洛搜索，以找到其他高分序列。作为设计方法的测试，发现基于GCN4-P1的同型二聚体线圈的埋疏水残基发现了高分序列。合成相应的肽并通过CD光谱和尺寸 - 排斥色谱法进行了特征。所有肽均为二聚体，在1摄氏度下近100％螺旋形，熔化的温度范围从24度C到57度。对设计的肽进行了定量结构活动关系分析，并发现与表面积埋葬的显着相关性。将埋入的表面积的融合到序列的评分中大大提高了预测和测量稳定性之间的相关性，并在完整的设计周期中证明了实验反馈。
We have conceived and implemented a cyclical protein design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control protein structure are the primary obstacles in protein design. Our protein design automation algorithm objectively predicts protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead-End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry from the vast number of possible solutions. Rotamer sequences were scored for steric complementarity using a van der Waals potential. A Monte Carlo search was then executed, starting at the optimal sequence, in order to find other high-scoring sequences. As a test of the design methodology, high-scoring sequences were found for the buried hydrophobic residues of a homodimeric coiled coil based on GCN4-p1. The corresponding peptides were synthesized and characterized by CD spectroscopy and size-exclusion chromatography. All peptides were dimeric and nearly 100% helical at 1 degrees C, with melting temperatures ranging from 24 degrees C to 57 degrees C. A quantitative structure activity relation analysis was performed on the designed peptides, and a significant correlation was found with surface area burial. Incorporation of a buried surface area potential in the scoring of sequences greatly improved the correlation between predicted and measured stabilities and demonstrated experimental feedback in a complete design cycle.