MOTIVATION: Protein language models are critical for modeling antibody-antigen interactions, yet sequence-based affinity prediction remains a key challenge, particularly when structural data are scarce. Existing methods often struggle to fully exploit sequence information, limiting their applicability across diverse antibody formats such as single-domain antibodies (sdAbs). RESULTS: We propose dual-level protein representation for affinity prediction (DLP-Affinity), a dual-level deep learning framework for accurate sequence-based affinity prediction. It leverages two complementary modules: residue-to-residue to capture local interface contacts, and global stochastic projection embedding to represent global protein properties. Utilizing a fine-tuned protein language model, our approach achieves state-of-the-art performance on the general AB-Bind dataset (reducing mean absolute error by up to 20.9%) and delivers highly competitive results on the sdAb-DB dataset. This provides a robust tool for sequence-based antibody affinity prediction. AVAILABILITY AND IMPLEMENTATION: The source code and datasets for DLP-Affinity are freely available at https://github.com/Zy-Wang-bit/DLP_Affinity and archived on Zenodo at https://doi.org/10.5281/zenodo.18437656.

Liquid-liquid phase separation plays a critical role in cellular processes, including protein aggregation and RNA metabolism, by forming membraneless subcellular structures. Accurate identification of phase-separated proteins is essential for understanding and controlling these processes. Traditional identification methods are effective but often costly and time-consuming. The recent machine learning methods have reduced these costs, but most models are restricted to classifying scaffold and client proteins with limited experimental conditions. To address this limitation, we developed a Mamba-based encoder using contrastive learning that incorporates separation probability, protein type, and experimental conditions. Our model achieved 95.2% accuracy in predicting phase-separated proteins and an ROCAUC score of 0.87 in classifying scaffold and client proteins. Further validation in the DgHBP-2 drug delivery system demonstrated its potential for condition modulation in drug development. This study provides an effective framework for the accurate identification and control of phase separation, facilitating advancements in biomedical research and therapeutic applications.

The prediction of binding free energy changes ($\Delta \Delta G$) caused by mutations in protein complexes is crucial for understanding disease mechanisms and designing antibodies. Approximately 60% of pathogenic missense mutations lead to functional abnormalities by disrupting molecular interactions. However, although existing $\Delta \Delta G$ predictors exhibit strong performance in benchmarks, they suffer from inadequate generalization, a misalignment between evaluation metrics and practical needs, and poor adaptability to complex mutation scenarios. This study systematically assessed eight mainstream predictors, covering both physical energy function-based and machine learning-based methods, and constructed an independent evaluation set. This study employed multi-dimensional metrics, including regression accuracy and classification capability, while also analyzing the performance variations of predictors across different mutation types, stability categories, and microenvironments of protein mutation sites. The results indicate that >60% of predictors (5 out of 8) predictors exhibit a systematic bias toward overestimating mutational instability. In the three-class classification task, predictors demonstrate a limited ability to identify stabilizing mutations ($\Delta \Delta G< -0.5$ kcal/mol), with recall rates <0.1 for this class, and overall predictive efficacy depends on the protein local structure. In summary, this study reveals the limitations of current $\Delta \Delta G$ predictors in terms of generalization and adaptability to complex scenarios, thus providing a reference for the optimization and practical application of $\Delta \Delta G$ prediction methods. It suggests that future breakthroughs can be achieved by constructing balanced and standardized datasets alongside developing local-global fusion algorithms.

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.

The success of drug discovery relies on predicting the binding affinity of protein-ligand. Applying deep learning to this field can expedite the process and reduce resource consumption. Recently, researchers have employed graph neural networks for predicting protein-ligand binding affinitiy, showcasing remarkable performance. However, this is largely attributed to the natural representation of biomolecule by graph neural networks, rather than a rational modeling of interactions within protein-ligand complex. In this regard, we have developed an Equivariant Interaction-aware Graph Network (EIGN), capable of learning 3D geometric structural information of complex while perceiving interactions related to protein-ligand binding affinity between nodes. Specifically, we designed distance-inspired edge-gated attention layer for inter-node interactions within the complex, uniformly learning interactions within and between molecules. To precisely simulate interactions between nodes, we considered local structural information around nodes when interactions occur. Leveraging equivariant convolutional layer to harness the advantages of learning geometric structure and drawing insights from existing work, we developed EIGN. Demonstrated on two benchmark sets, EIGN presents exceptional performance and generalization, highlighting the importance of accurate interaction modeling in drug discovery.

BACKGROUND: Natural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification. RESULTS: This study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures. CONCLUSION: TPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.