4.7 Article

LPTD: a novel linear programming-based topology determination method for cryo-EM maps

期刊

BIOINFORMATICS
卷 38, 期 10, 页码 2734-2741

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OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac170

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资金

  1. NIH Academic Research Enhancement Award [R15 AREA: 1R15GM126509 01]

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This article proposes a linear programming-based topology determination method to solve the secondary structure topology problem in three-dimensional geometrical space. It transforms the secondary structure matching problem into a complete weighted bipartite graph matching problem and uses linear programming algorithm to extract the true topology.
Topology determination is one of the most important intermediate steps toward building the atomic structure of proteins from their medium-resolution cryo-electron microscopy (cryo-EM) map. The main goal in the topology determination is to identify correct matches (i.e. assignment and direction) between secondary structure elements (SSEs) (alpha-helices and beta-sheets) detected in a protein sequence and cryo-EM density map. Despite many recent advances in molecular biology technologies, the problem remains a challenging issue. To overcome the problem, this article proposes a linear programming-based topology determination (LPTD) method to solve the secondary structure topology problem in three-dimensional geometrical space. Through modeling of the protein's sequence with the aid of extracting highly reliable features and a distance-based scoring function, the secondary structure matching problem is transformed into a complete weighted bipartite graph matching problem. Subsequently, an algorithm based on linear programming is developed as a decision-making strategy to extract the true topology (native topology) between all possible topologies. The proposed automatic framework is verified using 12 experimental and 15 simulated alpha-beta proteins. Results demonstrate that LPTD is highly efficient and extremely fast in such a way that for 77% of cases in the dataset, the native topology has been detected in the first rank topology in <2 s. Besides, this method is able to successfully handle large complex proteins with as many as 65 SSEs. Such a large number of SSEs have never been solved with current tools/methods.

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