EMDB/PDB/SASBDBから引用されている文献のデータベース

キーワード
構造解析手法	All X線回折 NMR 電子顕微鏡小角散乱中性子線回折線維回折粉末回折赤外分光 EPR FRET 理論モデルハイブリッド
著者・登録者
ジャーナル
IF	>30 >20 >10 >5 all

タイトル	A suite of designed protein cages using machine learning and protein fragment-based protocols.
ジャーナル・号・ページ	Structure, Year 2024
掲載日	2024年3月19日
著者	Kyle Meador / Roger Castells-Graells / Roman Aguirre / Michael R Sawaya / Mark A Arbing / Trent Sherman / Chethaka Senarathne / Todd O Yeates /
PubMed 要旨	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.
リンク	Structure / PubMed:38513658
手法	EM (単粒子) / X線回折
解像度	2.02 - 6 Å
構造データ	42181 8uf0 EMDB-42181, PDB-8uf0: T33-ml23 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 2.02 Å 42286 8ui2 EMDB-42286, PDB-8ui2: T33-ml28 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 2.73 Å 42355 8ukm EMDB-42355, PDB-8ukm: T33-ml30 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 4.2 Å 42381 8ump EMDB-42381, PDB-8ump: T33-ml35 - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 2.92 Å 42382 8umr EMDB-42382, PDB-8umr: T33-ml35 Assembly Intermediate - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 4.42 Å 42390 8un1 EMDB-42390, PDB-8un1: T33-ml23 Assembly Intermediate - Designed Tetrahedral Protein Cage Using Machine Learning Algorithms 手法: EM (単粒子) / 解像度: 3.9 Å PDB-8uja: T33-fn10 - Designed Tetrahedral Protein Cage Using Fragment-based Hydrogen Bond Networks 手法: X-RAY DIFFRACTION / 解像度: 6.0 Å
化合物	ChemComp-HOH: WATER / 水
由来	synthetic construct (人工物) sulfurisphaera tokodaii str. 7 (古細菌) novosphingobium aromaticivorans dsm 12444 (バクテリア)
キーワード	DE NOVO PROTEIN (De novo) / Nanohedra / protein cage / tetrahedral (三角錐) / de novo protein interface (De novo) / machine learning (機械学習) / two components / ProteinMPNN / nanoparticle (ナノ粒子) / tetrahedral nanoparticle / designed protein (デザイン) / de novo interface / two-component complex / Rosetta