9rpl | pdb_00009rpl

Lactobacillus acidophilus SlpA self assembly domains I-II (aa 49-308)

spinqualitylabelsall models 9rpl, resolution 1.93Å Show:Asymmetric Unit Biological Assembly Drag the structure with the mouse to rotate   Export Animated Image   Print3D Structural highlights 9rpl is a 2 chain structure with sequence from Lactobacillus acidophilus NCFM. Full crystallographic information is available from OCA. For a guided tour on the structure components use FirstGlance. Method: X-ray diffraction, Resolution 1.93Å Ligands: FMT, GOL Resources: FirstGlance, OCA, PDBe, RCSB, PDBsum, ProSAT Publication Abstract from PubMed Structural predictions have reached unprecedented accuracy. They leverage sequence-specific data to capture all potential interactions a sequence has evolved to fulfill. AlphaFold derives information from three sources: learned parameters capturing intrinsic amino acid secondary structure and environment propensity; models of related proteins providing structural templates; and aligned sequences encoding profiles and concerted evolutionary changes of residues involved in contacts. However, function demands dynamic changes; hence not all possible interactions can coexist simultaneously. Comprehensive information entails contradictions, which resolved in favor of the better-informed structure will silence less stable states and associations. Here, we introduce a method using all three channels to include prior knowledge: site-specific variants, predefined alignments and templates. Selecting information relevant to a particular state delimits the functional context of a prediction. Our program VAIRO allows us to rescue asymmetric and weaker interactions to complete the view of molecular assemblies in the architecture of a bacterial surface layer, and reveals otherwise inaccessible dynamic states in a pneumococcal multimeric membrane protein complex. VAIRO is distributed via the python package index (PyPI) (https://pypi.org/project/vairo) and the code is also available on Github (https://github.com/arcimboldo-team/vairo). Guiding AlphaFold predictions with experimental knowledge to inform dynamics and interactions with VAIRO.,Trivino J, Jimenez E, Grininger C, Caballero I, Medina A, Castellvi A, Petrillo G, Govantes F, Sagmeister T, Alcorlo M, Hermoso JA, Sammito MD, Diederichs K, Pavkov-Keller T, Uson I Protein Sci. 2026 Feb;35(2):e70481. doi: 10.1002/pro.70481. PMID:41578971 From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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