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<p>We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which uses a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. Testing on the high-quality HINT database of binary PPIs reveals that the integrated method has better performance and identifies more high-confidence interactions than any of the component methods. The AF3Complex algorithm is used to predict the structures of 374 PPIs with a large fraction having at least partially overlapping interfaces with PrePPI models of the same complex. Clustering of the high-confidence <i>E. coli</i> interactome yields 385 subnetworks which have high functional coherence. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail.</p>

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Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • H. Zhao,
  • C. Velez,
  • A. Naravane,
  • A. Saha,
  • J. Feldman,
  • J. Skolnick,
  • D. Murray,
  • B. Honig

摘要

We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which uses a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. Testing on the high-quality HINT database of binary PPIs reveals that the integrated method has better performance and identifies more high-confidence interactions than any of the component methods. The AF3Complex algorithm is used to predict the structures of 374 PPIs with a large fraction having at least partially overlapping interfaces with PrePPI models of the same complex. Clustering of the high-confidence E. coli interactome yields 385 subnetworks which have high functional coherence. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail.