Oren Ersin Emre, Tamerler Candan, Sahin Deniz, Hnilova Marketa, Seker Urartu Ozgur Safak, Sarikaya Mehmet, Samudrala Ram
Materials Science and Engineering Department, University of Washington, Seattle, USA.
Bioinformatics. 2007 Nov 1;23(21):2816-22. doi: 10.1093/bioinformatics/btm436. Epub 2007 Sep 17.
The discovery of solid-binding peptide sequences is accelerating along with their practical applications in biotechnology and materials sciences. A better understanding of the relationships between the peptide sequences and their binding affinities or specificities will enable further design of novel peptides with selected properties of interest both in engineering and medicine.
A bioinformatics approach was developed to classify peptides selected by in vivo techniques according to their inorganic solid-binding properties. Our approach performs all-against-all comparisons of experimentally selected peptides with short amino acid sequences that were categorized for their binding affinity and scores the alignments using sequence similarity scoring matrices. We generated novel scoring matrices that optimize the similarities within the strong-binding peptide sequences and the differences between the strong- and weak-binding peptide sequences. Using the scoring matrices thus generated, a given peptide is classified based on the sequence similarity to a set of experimentally selected peptides. We demonstrate the new approach by classifying experimentally characterized quartz-binding peptides and computationally designing new sequences with specific affinities. Experimental verifications of binding of these computationally designed peptides confirm our predictions with high accuracy. We further show that our approach is a general one and can be used to design new sequences that bind to a given inorganic solid with predictable and enhanced affinity.
随着固相结合肽序列在生物技术和材料科学中的实际应用不断加速，对其发现也越来越多。更好地理解肽序列与其结合亲和力或特异性之间的关系，将有助于在工程和医学领域进一步设计出具有特定感兴趣特性的新型肽。
开发了一种生物信息学方法，根据其无机固相结合特性对通过体内技术筛选出的肽进行分类。我们的方法对实验选择的短氨基酸序列肽进行全对全比较，根据其结合亲和力对这些序列进行分类，并使用序列相似性评分矩阵对序列比对进行评分。我们生成了新的评分矩阵，以优化强结合肽序列内的相似性以及强结合与弱结合肽序列之间的差异。使用由此生成的评分矩阵，根据与一组实验选择肽的序列相似性对给定肽进行分类。我们通过对实验表征的石英结合肽进行分类并通过计算设计具有特定亲和力的新序列来展示这种新方法。这些通过计算设计的肽的结合实验验证以高精度证实了我们的预测。我们进一步表明，我们的方法具有通用性，可用于设计以可预测且增强的亲和力与给定无机固体结合的新序列。
