It is our great pleasure to welcome Linqing and Keji to join our research group. Bringing diverse expertise, they will bring valuable insights to our team. We warmly welcome them on board!

Zefeng's work on the geometric and probabilistic modeling of protein backbones has been published in J. Chem. Inf. Model (link). This study proposed using a concise protein backbone representation and demonstrated peptide units' 3D rotations that unify the angular parts of internal coordinates on a single rotation manifold, composing the primary degrees of freedom of the protein backbone. This representation enables efficient differentiable coordinate conversions, effective conformation optimization ability free from the lever-arm effect, and straightforward geometric reasoning capability within deep neural networks. By integrating it into protein backbone generative models, we achieved enhanced performance in protein design regarding in silico stereochemical quality, diversity, novelty, and length generalizability. We anticipate its future application in advancing biomolecular structure prediction and design. Congratulations!

The 4th National Conference on Biomolecular Structure Prediction and Modeling was held in Qingdao from June 26 to 29, 2026, with several members of our research group in attendance. Jiajun delivered an oral presentation of our latest work, SteerAF: distogram-based steering of AlphaFold2 toward alternative conformations, and was awarded the second prize for oral reports. Ruihan and Qiushi received the third prize in the poster section. We extend our sincere congratulations to both!

Our latest membrane contact probability (MCP) predictor, PepMCP, has been published in Bioinformatics (link). PepMCP is a novel peptide-tailored model that predicts MCP and identifies membrane-lytic antimicrobial peptides (AMPs). We trained PepMCP on data from more than 500 literature-sourced membrane-lytic AMPs. It leverages coarse-grained molecular dynamics simulations to extract residue-level MCP features for model training. PepMCP can also be used in a binary classification mode, enabling efficient identification of membrane-lytic AMPs. The study also releases MemAMPdb, a curated membrane-lytic AMP database, together with a publicly accessible PepMCP web server, to support global academic research. Several group members participated in the research. Ruihan constructed the model, and Tadsanee ran the large-scale MD simulations. Congratulations!

We are delighted to welcome Dr. Yi Ren to the group. Having earned his PhD, he joins us as a postdoctoral researcher. We look forward to his future contributions. Welcome aboard!

Hongyang and Zhouyu successfully completed their undergraduate thesis defenses on May 29 and June 5, 2026, respectively. Congratulations!

Kai defended his PhD viva on May 25, 2026, while Jingze and Lingfeng completed theirs on May 26, 2026. Their defenses proceeded smoothly. Congratulations to all three! The whole group joins in celebrating this wonderful achievement.

Zhongjie has completed his postdoctoral research and left our group to take up a new role. During his time here, he made outstanding contributions to the development of the membrane-aware anisotropic network model (MCP-ANM). He has joined Beijing Sun-Novo Pharmaceutical Research Co., Ltd. as a director of innovative drug research. We wish him every success in his future career.

Our collaborative study with Prof. Xiao-Wei Chen' lab and Prof. Ning Gao' lab has been published online in Molecular Cell (link). Using MD simulations, we uncovered specific phosphatidylinositol (PI)-binding pockets on the Erlin1/2 complex at the ER membrane luminal leaflet. PI binding is critical for the complex’s stability and assembly. Congratulations to Tadsanee!

Zhongjie's work on membrane protein dynamics has been published in PRX Life (link). The study introduces MCP-ANM, a membrane-aware anisotropic network model that incorporates membrane contact probability (MCP) to better describe membrane-induced constraints in protein dynamics. The method significantly improves flexibility prediction for membrane proteins compared with conventional ANM approaches. By integrating MCP-ANM with perturbation response scanning (PRS), the framework efficiently simulates mechanosensitive gating mechanisms, reproducing both force-from-lipids (e.g., MscS, PIEZO) and force-from-tether (e.g., NOMPC) models. The predicted mechanosensitivity agrees with experimental observations, while requiring only seconds to minutes per system. Congratulations!