PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models

Abstract

Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities.

Keywords: Bind affinity prediction; ESM; Graph transformer.

Fig. 1

The framework for protein-protein affinity prediction using pre-trained models and the PPI-Graphomer module. A The workflow of this study involves: utilizing ESM2 to extract sequence representations of protein complexes and ESM-IF1 to extract structural representations, which are subsequently concatenated. The concatenated representations are then processed through the PPI-Graphomer module to obtain interface representations. These interface representations are further concatenated with the original features. Ultimately, a MLP is employed to predict affinity values quantitatively. B The architecture of PPI-Graphomer incorporates bias terms based on three different encodings into the attention matrix, which are then multiplied by distance-based weight coefficients. C Three encodings based on amino acid structural information are utilized: encoding of amino acid pair types, encoding based on intermolecular interaction forces, and a masking matrix based on interface information

Fig. 2

The density plots of the $\mathrm{\Delta }$G for the training set and two test sets. The x-axis represents the label values, while the y-axis denotes the probability distribution of the labels. The choice of a density plot over a histogram is attributed to the former’s ability to provide a smoother distribution estimation and facilitate the observation of distributions from datasets of varying sizes within the same figure

Fig. 3

The Scatter plot of predicted vs experimental binding affinities. The model performance was validated on two separate test datasets. The first test set comprised 75 samples, yielding a PCC of 0.641 and a MAE of 1.64. The second test set included 87 samples, achieving a PCC of 0.625 and an MAE of 1.51