Database of articles cited by EMDB/PDB/SASBDB data

Keywords
Structure methods	All X-ray diffraction NMR electron microscopy small angle scattering neutron diffraction fiber diffraction powder diffraction infrared spectroscopy EPR fluorescence transfer theoretical model Hybrid
Author
Journal
IF	>30 >20 >10 >5 all

Title	A suite of designed protein cages using machine learning and protein fragment-based protocols.
Journal, issue, pages	Structure, Vol. 32, Issue 6, Page 751-765.e11, Year 2024
Publish date	Jun 6, 2024
Authors	Kyle Meador / Roger Castells-Graells / Roman Aguirre / Michael R Sawaya / Mark A Arbing / Trent Sherman / Chethaka Senarathne / Todd O Yeates /
PubMed Abstract	Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational ...Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.
External links	Structure / PubMed:38513658 / PubMed Central
Methods	EM (single particle) / X-ray diffraction
Resolution	2.02 - 6 Å
Structure data	42181 8uf0 EMDB-42181, PDB-8uf0: T33-ml23 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 2.02 Å 42286 8ui2 EMDB-42286, PDB-8ui2: T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 2.73 Å 42355 8ukm EMDB-42355, PDB-8ukm: T33-ml30 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 4.2 Å 42381 8ump EMDB-42381, PDB-8ump: T33-ml35 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 2.92 Å 42382 8umr EMDB-42382, PDB-8umr: T33-ml35 Assembly Intermediate - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 4.42 Å 42390 8un1 EMDB-42390, PDB-8un1: T33-ml23 Assembly Intermediate - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms Method: EM (single particle) / Resolution: 3.9 Å PDB-8uja: T33-fn10 - Designed Tetrahedral Protein Cage Using Fragment-based Hydrogen Bond Networks Method: X-RAY DIFFRACTION / Resolution: 6 Å
Chemicals	ChemComp-HOH: WATER
Source	synthetic construct (others) sulfurisphaera tokodaii str. 7 (archaea) novosphingobium aromaticivorans dsm 12444 (bacteria)
Keywords	DE NOVO PROTEIN / Nanohedra / protein cage / tetrahedral / de novo protein interface / machine learning / two components / ProteinMPNN / nanoparticle / tetrahedral nanoparticle / designed protein / de novo interface / two-component complex / Rosetta