Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
  Multi-Chain Protein-Protein Interactions Prediction
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.
6835523ac0003bc4022e0113
Hazem Alsamkary
claimed_verified
2025-05-28T12:12:12.065Z
61faa58a11deaebce017da4a
https://cdn-avatars.huggingface.co/v1/production/uploads/1670851610124-61faa58a11deaebce017da4a.jpeg
Hazem Essam
user
hazemessam
6835523ac0003bc4022e0114
Mohamed Elshaffei
admin_assigned
2025-05-28T12:26:53.712Z
63959596401bd73e467e1fc2
/avatars/963d073e9d5ee414d5c185faf9d8d4a6.svg
Mohamed Elshaffei
user
shaffei
6835523ac0003bc4022e0115
Mohamed Soudy
admin_assigned
2025-05-28T12:26:59.250Z
63a1923af30c4642277456e6
/avatars/d1f06afe2f4e3cb30d014045d8c62c28.svg
Mohamed Soudy
user
MohmedSoudy
6835523ac0003bc4022e0116
Sara Ossman
6835523ac0003bc4022e0117
Abdallah Amr
6835523ac0003bc4022e0118
Nehal Adel Abdelsalam
6835523ac0003bc4022e0119
Mohamed Elkerdawy
admin_assigned
2025-05-28T12:27:15.295Z
62f96db84588fe31f43aceb9
/avatars/7577f071b5a030f04f6ab0df46e5e246.svg
Mohamed Elkerdawy
user
melkerdawy
6835523ac0003bc4022e011a
Ahmed Elnaggar
admin_assigned
2025-05-28T12:27:28.070Z
5f16d73c925b9863e28ad430
/avatars/2f61e47eb057effee4490d62a67371c6.svg
Ahmed Elnaggar
user
agemagician
Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
  Multi-Chain Protein-Protein Interactions Prediction
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.
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.
protein language models PPI binding affinity prediction embeddings concatenation sequences concatenation hierarchical pooling pooled attention addition ProtT5 ESM2 Ankh Ankh2 ESM3 Spearman correlation