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.

Cryo-electron microscopy single particle analysis (cryo-EM SPA) is the most powerful technique for biomacromolecule structure determination. However, many factors such as complicated noise and radiation damage make the quality of cryo-EM images extremely poor, where high-frequency structure details are submerged, limiting the application of deep learning and suppressing the resolution of reconstruction. Thus, image restoration is of vital importance. Some related works explore micrograph restoration, but the particles in restored micrographs are still of poor quality. Moreover, the training approach of existing methods uses noisy observations or simulated data as supervision, leading to reduced performance on real cryo-EM data. In this paper, we define the task of particle restoration and propose a novel 4-step framework to this end. Labels are created for each particle image and paired data is collected within our framework, compensating for the absence of ground truth. A deep neural network with encoder-decoder architecture is designed to learn the mapping from degraded particles to high-quality ones, while other networks can also be employed as a plug-and-play module. Three datasets are constructed from real cryo-EM data and extensive experiments are carried out. Both quantitative metrics and qualitative visualization indicate that our framework is effective for cryo-EM particle restoration. It becomes easier to extract particle features after restoration, aiding in SPA and the effective application of deep learning on cryo-EM images. The downstream task experiments of cryo-EM SPA are also conducted, showing that the proposed framework has the potential to improve cryo-EM SPA performance.

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.

Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.

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.