Nhat Truong Pham

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.

Large language models (LLMs) have significantly advanced computational biology by enabling the integration of molecular, protein, and natural language data to accelerate drug discovery. However, existing molecular captioning approaches often underutilize diverse molecular modalities and lack interpretability. In this study, we introduce XMolCap, a novel explainable molecular captioning framework that integrates molecular images, SMILES strings, and graph-based structures through a stacked multimodal fusion mechanism. The framework is built upon a BioT5-based encoder-decoder architecture, which serves as the backbone for extracting feature representations from SELFIES. By leveraging specialized models such as SwinOCSR, SciBERT, and GIN-MoMu, XMolCap effectively captures complementary information from each modality. Our model not only achieves state-of-the-art performance on two benchmark datasets (L+M-24 and ChEBI-20), outperforming several strong baselines, but also provides detailed, functional group-aware, and property-specific explanations through graph-based interpretation. XMolCap is publicly available at https://github.com/cbbl-skku-org/XMolCap/ for reproducibility and local deployment. We believe it holds strong potential for clinical and pharmaceutical applications by generating accurate, interpretable molecular descriptions that deepen our understanding of molecular properties and interactions.

A time-traveling is generated and experimentally confirmed using a relativistic circle constructed by a microstrip add-drop multiplexer. Such a device can operate with both an antenna and electronic device functions, which can be applied to link the two different time events with wired or wireless transmission schemes. A timeline is formed by the Rabi frequency oscillation when entangled states under successive filtering. In this letter, the message can be modulated for the lower and upper sidebands of the Rabi frequency, presenting the past and future traveling times, respectively. The required information can be retrieved in the past or the future via point-to-point transmission terminals. In the experiment, the message is modulated by the required information to the frequency-time function of the present carrier and input into the system performed by a point-to-point link. The merging information is formed on the timeline, and can be retrieved via the device output ports. The band pass filter is applied to obtain the required message. The results have shown that the proposed work can be realized with point-to-point communication, where the collapsed entangled pairs from both sides of the worlds are merged then Bob can communicate with Alice via the timeline.