Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
  Multi-Chain Protein-Protein Interactions Prediction
Authors: name: Hazem Alsamkary; name: Mohamed Elshaffei; name: Mohamed Soudy; name: Sara Ossman; name: Abdallah Amr; name: Nehal Adel Abdelsalam; name: Mohamed Elkerdawy; name: Ahmed Elnaggar
Published: 2025-05-26 14:23:08

Abstract: Protein-protein interactions (PPIs) are fundamental to numerous cellular
processes, and their characterization is vital for understanding disease
mechanisms and guiding drug discovery. While protein language models (PLMs)
have demonstrated remarkable success in predicting protein structure and
function, their application to sequence-based PPI binding affinity prediction
remains relatively underexplored. This gap is often attributed to the scarcity
of high-quality, rigorously refined datasets and the reliance on simple
strategies for concatenating protein representations. In this work, we address
these limitations. First, we introduce a meticulously curated version of the
PPB-Affinity dataset of a total of 8,207 unique protein-protein interaction
entries, by resolving annotation inconsistencies and duplicate entries for
multi-chain protein interactions. This dataset incorporates a stringent, less
than or equal to 30%, sequence identity threshold to ensure robust splitting
into training, validation, and test sets, minimizing data leakage. Second, we
propose and systematically evaluate four architectures for adapting PLMs to PPI
binding affinity prediction: embeddings concatenation (EC), sequences
concatenation (SC), hierarchical pooling (HP), and pooled attention addition
(PAD). These architectures were assessed using two training methods: full
fine-tuning and a lightweight approach employing ConvBERT heads over frozen PLM
features. Our comprehensive experiments across multiple leading PLMs (ProtT5,
ESM2, Ankh, Ankh2, and ESM3) demonstrated that the HP and PAD architectures
consistently outperform conventional concatenation methods, achieving up to 12%
increase in terms of Spearman correlation. These results highlight the
necessity of sophisticated architectural designs to fully exploit the
capabilities of PLMs for nuanced PPI binding affinity prediction.