title: Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
    Multi-Chain Protein-Protein Interactions Prediction
summary: 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.
paper_authors._id: 6835523ac0003bc4022e0113
paper_authors.name: Hazem Alsamkary
paper_authors.status: claimed_verified
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paper_authors.fullname: Hazem Essam
paper_authors.type: user
paper_authors.user: hazemessam
paper_authors._id: 6835523ac0003bc4022e0114
paper_authors.name: Mohamed Elshaffei
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/963d073e9d5ee414d5c185faf9d8d4a6.svg
paper_authors.fullname: Mohamed Elshaffei
paper_authors.type: user
paper_authors.user: shaffei
paper_authors._id: 6835523ac0003bc4022e0115
paper_authors.name: Mohamed Soudy
paper_authors.status: admin_assigned
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paper_authors._id: 63a1923af30c4642277456e6
paper_authors.avatarUrl: /avatars/d1f06afe2f4e3cb30d014045d8c62c28.svg
paper_authors.fullname: Mohamed Soudy
paper_authors.type: user
paper_authors.user: MohmedSoudy
paper_authors._id: 6835523ac0003bc4022e0116
paper_authors.name: Sara Ossman
paper_authors._id: 6835523ac0003bc4022e0117
paper_authors.name: Abdallah Amr
paper_authors._id: 6835523ac0003bc4022e0118
paper_authors.name: Nehal Adel Abdelsalam
paper_authors._id: 6835523ac0003bc4022e0119
paper_authors.name: Mohamed Elkerdawy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:27:15.295Z
paper_authors._id: 62f96db84588fe31f43aceb9
paper_authors.avatarUrl: /avatars/7577f071b5a030f04f6ab0df46e5e246.svg
paper_authors.fullname: Mohamed Elkerdawy
paper_authors.type: user
paper_authors.user: melkerdawy
paper_authors._id: 6835523ac0003bc4022e011a
paper_authors.name: Ahmed Elnaggar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:27:28.070Z
paper_authors._id: 5f16d73c925b9863e28ad430
paper_authors.avatarUrl: /avatars/2f61e47eb057effee4490d62a67371c6.svg
paper_authors.fullname: Ahmed Elnaggar
paper_authors.type: user
paper_authors.user: agemagician
paper_title: Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
    Multi-Chain Protein-Protein Interactions Prediction
paper_summary: 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.
paper_ai_summary: The study introduces a curated PPB-Affinity dataset and evaluates four architectural designs for adapting protein language models to predict protein-protein interaction binding affinity, demonstrating that hierarchical pooling and pooled attention addition architectures perform better than concatenation methods.
paper_ai_keywords: protein language models PPI binding affinity prediction embeddings concatenation sequences concatenation hierarchical pooling pooled attention addition ProtT5 ESM2 Ankh Ankh2 ESM3 Spearman correlation