Protein structure prediction is a long‐standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman‐Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM‐align alignments initialized with the Needleman‐Wunsch residue descriptor alignments to standard TM‐align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full‐length searches to identify structural motifs within proteins. We compare the results of RUPEE to the protein structure searches mTM‐align, SSM, CATHEDRAL, and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to TM‐score as well as scores specific to SSM and CATHEDRAL, Q‐score and SSAP‐score, respectively.

中文翻译：

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蛋白质结构搜索支持蛋白质结构预测方法的发展

蛋白质结构预测是分子生物学中一个长期未解决的问题，随着最近在 CASP13 上使用 AlphaFold 进行深度学习的成功，它重新引起了人们的兴趣。在开发和评估蛋白质结构预测方法时，研究人员可能希望确定与其预测结构最相似的已知结构。这些预测的结构通常与已知结构的序列和结构相似性较低。我们展示了 RUPEE（一种纯几何蛋白质结构搜索）如何能够识别与结构预测最相似的结构，而不管它们与已知结构有何不同，这是现有蛋白质结构搜索所面临的问题。RUPEE 通过使用蛋白质结构的新型线性编码作为残基描述符序列来实现这一点。使用快速 Needleman-Wunsch 算法，RUPEE 能够对每个可用结构的残基描述符序列进行比对。随后是一系列越来越准确的结构比对，从使用 Needleman-Wunsch 残基描述符比对初始化的 TM 比对到最终结果的标准 TM 比对。通过在每个阶段有效地使用比对归一化，RUPEE 还可以执行包含搜索以及全长搜索以识别蛋白质中的结构基序。我们使用来自提交给 CASP13 的蛋白质结构预测的基准，将 RUPEE 的结果与蛋白质结构搜索 mTM-align、SSM、CATHEDRAL 和 VAST 进行比较。