MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction
  Prediction via Microenvironment-Aware Protein Embedding
Lirong Wu Yijun Tian Yufei Huang Siyuan Li Haitao Lin Nitesh V Chawla Stan Z. Li
Protein-Protein Interactions (PPIs) are fundamental in various biological
processes and play a key role in life activities. The growing demand and cost
of experimental PPI assays require computational methods for efficient PPI
prediction. While existing methods rely heavily on protein sequence for PPI
prediction, it is the protein structure that is the key to determine the
interactions. To take both protein modalities into account, we define the
microenvironment of an amino acid residue by its sequence and structural
contexts, which describe the surrounding chemical properties and geometric
features. In addition, microenvironments defined in previous work are largely
based on experimentally assayed physicochemical properties, for which the
"vocabulary" is usually extremely small. This makes it difficult to cover the
diversity and complexity of microenvironments. In this paper, we propose
Microenvironment-Aware Protein Embedding for PPI prediction (MPAE-PPI), which
encodes microenvironments into chemically meaningful discrete codes via a
sufficiently large microenvironment "vocabulary" (i.e., codebook). Moreover, we
propose a novel pre-training strategy, namely Masked Codebook Modeling (MCM),
to capture the dependencies between different microenvironments by randomly
masking the codebook and reconstructing the input. With the learned
microenvironment codebook, we can reuse it as an off-the-shelf tool to
efficiently and effectively encode proteins of different sizes and functions
for large-scale PPI prediction. Extensive experiments show that MAPE-PPI can
scale to PPI prediction with millions of PPIs with superior trade-offs between
effectiveness and computational efficiency than the state-of-the-art
competitors.