PhoSARte: identification of SARS-CoV-2 phosphorylation sites using contrastive learning and protein language models.

Abstract

Phosphorylation, a critical post-translational modification, is extensively altered during viral infections, including SARS-CoV-2, where it plays a central role in modulating host-pathogen interactions. Accurately identifying these specific phosphorylation sites is crucial for understanding viral pathogenesis, prioritizing antiviral targets, guiding therapeutic strategies, and strengthening preparedness for future viral outbreaks. Although several computational tools have been proposed to complement experimental phosphoproteomics, existing methods often show limited robustness, cross-viral generalizability, and interpretability. To address these challenges, we developed PhoSARte, an interpretable computational framework that integrates Siamese network-based contrastive learning (SCL) with pretrained protein language models (PLMs) to accurately identify phosphorylation sites in SARS-CoV-2-infected cells. PhoSARte employs a unique dual-stream architecture: PLMs capture contextual protein sequence representations, while the SCL module, comprising a transformer-based encoder and an attention-based bidirectional gated recurrent unit, learns discriminative and similarity-preserving representations of protein sequence pairs using a contrastive loss function. The integration of these complementary representations substantially improves the robustness and generalizability of the framework. PhoSARte was rigorously benchmarked on phosphoproteomics datasets derived from infected A549 (Homo sapiens) and Vero E6 (Chlorocebus sabaeus) cells, as well as their combined dataset. Through rigorous cross-cell-type validation and testing, PhoSARte demonstrated superior performance, significantly outperforming current state-of-the-art methods. Importantly, an external cross-viral case study on entirely unseen adenovirus type 2-infected human IMR-90 cells confirmed the broad transferability of PhoSARte, demonstrating its capacity to generate actionable biological hypotheses under novel viral stress conditions. Furthermore, advancing beyond traditional black-box predictors, PhoSARte integrates an in silico mutagenesis analysis that decodes complex deep learning embeddings to successfully extract biologically relevant motif signatures. PhoSARte is freely accessible at https://balalab-skku.org/PhoSARte/, providing an accessible, interpretable, and adaptable framework for virus-associated phosphorylation site prediction, antiviral target prioritization, and host-directed therapeutic discovery.