PDeepPP:A Deep learning framework with Pretrained Protein language for peptide classification

Protein post-translational modifications (PTMs) and bioactive peptides (BPs) play critical roles in various biological processes and have significant therapeutic potential. However, identifying PTM sites and bioactive peptides through experimental methods is often labor-intensive, costly, and time-consuming. As a result, computational tools, particularly those based on deep learning, have become effective solutions for predicting PTM sites and peptide bioactivity. Despite progress in this field, existing methods still struggle with the complexity of protein sequences and the challenge of requiring high-quality predictions across diverse datasets. To address these issues, we propose a deep learning framework that integrates pretrained protein language models with a neural network combining transformer and CNN for peptide classification. By leveraging the ability of pretrained models to capture complex relationships within protein sequences, combined with the predictive power of parallel networks, our approach improves feature extraction while enhancing prediction accuracy. This framework was applied to multiple tasks involving PTM site and bioactive peptide prediction, utilizing large-scale datasets to enhance the model's robustness. In the comparison across 33 tasks, the model achieved state-of-the-art (SOTA) performance in 25 of them, surpassing existing methods and demonstrating its versatility across different datasets. Our results suggest that this approach provides a scalable and effective solution for large-scale peptide discovery and PTM analysis, paving the way for more efficient peptide classification and functional annotation.

KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models

Currently, little research has been done on knowledge editing for Large Vision-Language Models (LVLMs). Editing LVLMs faces the challenge of effectively integrating diverse modalities (image and text) while ensuring coherent and contextually relevant modifications. An existing benchmark has three metrics (Reliability, Locality and Generality) to measure knowledge editing for LVLMs. However, the benchmark falls short in the quality of generated images used in evaluation and cannot assess whether models effectively utilize edited knowledge in relation to the associated content. We adopt different data collection methods to construct a new benchmark, $\textbf{KEBench}$, and extend new metric (Portability) for a comprehensive evaluation. Leveraging a multimodal knowledge graph, our image data exhibits clear directionality towards entities. This directional aspect can be further utilized to extract entity-related knowledge and form editing data. We conducted experiments of different editing methods on five LVLMs, and thoroughly analyze how these methods impact the models. The results reveal strengths and deficiencies of these methods and, hopefully, provide insights into potential avenues for future research.

PTransIPs: Identification of phosphorylation sites based on protein pretrained language model and Transformer

Phosphorylation is central to numerous fundamental cellular processes, influencing the onset and progression of a variety of diseases. The correct identification of these phosphorylation sites is of great importance to unravel the intricate molecular mechanisms within cells and during viral infections, potentially leading to the discovery of new therapeutic targets. In this study, we introduce PTransIPs, a novel deep learning model for the identification of phosphorylation sites. PTransIPs treat amino acids within protein sequences as words, extracting unique encodings based on their type and sequential position. The model also incorporates embeddings from large pretrained protein models as additional data inputs. PTransIPS is further trained on a combination model of convolutional neural network with residual connections and Transformer model equipped with multi-head attention mechanisms. At last, the model outputs classification results through a fully connected layer. The results of independent testing reveal that PTransIPs outperforms existing state-of-the-art(SOTA) methods, achieving AUROCs of 0.9232 and 0.9660 for identifying phosphorylated S/T and Y sites respectively. In addition, ablation studies prove that pretrained model embeddings contribute to the performance of PTransIPs. Furthermore, PTransIPs has interpretable amino acid preference, visible training process and shows generalizability on other bioactivity classification tasks. To facilitate usage, our code and data are publicly accessible at \url{https://github.com/StatXzy7/PTransIPs}.