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!