序列数据空间的自适应BLAST：蛋白质序列嵌入理论
Adaptive BLASTing through the Sequence Dataspace: Theories on Protein Sequence Embedding
摘要：
我们认为系统发育谱提供了一种定量方法，可以将蛋白质的结构和功能特性以及它们的进化关系联系起来。系统发育谱的一个关键特征是可互操作的数据格式（例如比对信息、生理化学信息、基因组信息等）。事实上，我们以前已经证明了位置特异性评分矩阵（PSSM）是一个信息M维，可以从嵌入或未修饰的序列比对的定量测量进行评分。此外，从这些比对中获得的信息是有用的，即使在序列相似性的模糊区（<25%同一性）（1-5）。虽然强大，我们以前的嵌入策略遭受污染对齐（嵌入和未修改）和计算费用。在这里，我们描述了一个启发式嵌入策略（自适应GDDA-BLAST，Ada-BLAST）的逻辑和算法过程。Ada-BLAST平均快约19倍，与我们以前的方法具有相似的灵敏度。此外，我们提供的数据表明嵌入式对齐测量隔离二级结构元件和分类跨膜结构域的结构/功能的好处。我们推测，序列嵌入是多种方式之一，低身份比对可以测量和纳入高性能的PSSM为基础的系统发育概况。
英文摘要：
We theorize that phylogenetic profiles provide a quantitative method that can relate the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of phylogenetic profiles is the interoperable data format (e.g. alignment information, physiochemical information, genomic information, etc). Indeed, we have previously demonstrated Position Specific Scoring Matrices (PSSMs) are an informative M-dimension which can be scored from quantitative measure of embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, even in the twilight zone of sequence similarity (<25% identity)(1-5). Although powerful, our previous embedding strategy suffered from contaminating alignments(embedded AND unmodified) and computational expense. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy (Adaptive GDDA-BLAST, Ada-BLAST). Ada-BLAST on average up to ~19-fold faster and has similar sensitivity to our previous method. Further, we provide data demonstrating the benefits of embedded alignment measurements for isolating secondary structural elements and the classifying transmembrane-domain structure/function. We theorize that sequence-embedding is one of multiple ways that low-identity alignments can be measured and incorporated into high-performance PSSM-based phylogenetic profiles.
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