A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput single-molecule force spectroscopy
A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput single-molecule force spectroscopy
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DOI：
10.1093/bioinformatics/btl293
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发表时间：
2007-01-15
期刊：
影响因子：
5.8
通讯作者：
Schroeder, Michael
中科院分区：
文献类型：
--
作者：
Marsico, Annalisa;Labudde, Dirk;Schroeder, Michael
动机：膜蛋白的错误折叠在许多人类疾病中起着重要作用，如视网膜色素变性、遗传性耳聋和尿崩症。人们对膜蛋白知之甚少，因为只有很少的高分辨率结构。单分子力谱是一种新技术，它测量将蛋白质从膜中拉出所需的力。这样的力曲线包含了关于蛋白质结构、构象以及分子间和分子内力的有价值的信息。高通量力谱实验产生了数百条力曲线，其中有假的，也有好的，它们对应于不同的展开路径。人工分析这些数据是不一致和主观标注的瓶颈和来源。结果：我们提出了一种新的算法来识别伪曲线和代表不同展开路径的曲线。我们的算法分三个阶段进行：第一，通过降维来降低曲线中的噪声；第二，用动态规划对齐曲线并计算成对距离；第三，根据这些距离对曲线进行聚类。我们将我们的方法应用于手工精选的细菌视紫红质突变体P50A的135条力曲线的数据集。该算法对伪曲线和好曲线的识别成功率为81%，对展开路径的分类成功率为76%。因此，我们讨论了细菌视紫红质的五种不同的展开途径，包括三个主要的展开事件和几个次要的展开事件。最后，我们将折叠势垒与残基的守恒程度联系起来。总体而言，该算法解决了力光谱的瓶颈，并导致了更一致和更可重复性的结果，为高通量分析膜蛋白的结构特征铺平了道路。
Motivation: Misfolding of membrane proteins plays an important role in many human diseases such as retinitis pigmentosa, hereditary deafness and diabetes insipidus. Little is known about membrane proteins as there are only very few high-resolution structures. Single-molecule force spectroscopy is a novel technique, which measures the force necessary to pull a protein out of a membrane. Such force curves contain valuable information on the protein structure, conformation, and inter- and intra-molecular forces. High-throughput force spectroscopy experiments generate hundreds of force curves including spurious ones and good curves, which correspond to different unfolding pathways. Manual analysis of these data is a bottleneck and source of inconsistent and subjective annotation.Results: We propose a novel algorithm for the identification of spurious curves and curves representing different unfolding pathways. Our algorithm proceeds in three stages: first, we reduce noise in the curves by applying dimension reduction; second, we align the curves with dynamic programming and compute pairwise distances and third, we cluster the curves based on these distances. We apply our method to a hand-curated dataset of 135 force curves of bacteriorhodopsin mutant P50A. Our algorithm achieves a success rate of 81% distinguishing spurious from good curves and a success rate of 76% classifying unfolding pathways. As a result, we discuss five different unfolding pathways of bacteriorhodopsin including three main unfolding events and several minor ones. Finally, we link folding barriers to the degree of conservation of residues. Overall, the algorithm tackles the force spectroscopy bottleneck and leads to more consistent and reproducible results paving the way for high-throughput analysis of structural features of membrane proteins.