Atlantis: An integrative database for human proteome structural and functional sites
De Oliveira Rosa, N.; Ferronato, P.; Varisco, M.; Matic, M.; Ruscio, M.; Miglionico, P.; Raimondi, F.
Show abstract
Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks 94%
- ProTargetMiner: A proteome signature library of anticancer molecules for functional discovery 93%
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks 92%
Similar papers in this journal
- Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection 94%
- AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts 94%
- MutationExplorer - a webserver for mutation of proteins and 3D visualization of energetic impacts 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.