side chain orientation
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ChemBioChem ◽  
2020 ◽  
Author(s):  
Lennart Nicke ◽  
Philip Horx ◽  
Ronny Müller ◽  
Sylvia Els‐Heindl ◽  
Armin Geyer

2020 ◽  
Vol 21 (16) ◽  
pp. 5655
Author(s):  
Nanyu Han ◽  
Justin Tze Yang Ng ◽  
Yanpeng Li ◽  
Yuguang Mu ◽  
Zunxi Huang

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342–347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 μs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.


ChemMedChem ◽  
2019 ◽  
Vol 14 (21) ◽  
pp. 1849-1855 ◽  
Author(s):  
Lennart Nicke ◽  
Ronny Müller ◽  
Armin Geyer ◽  
Sylvia Els‐Heindl

Vaccine ◽  
2014 ◽  
Vol 32 (18) ◽  
pp. 2117-2126 ◽  
Author(s):  
Adriana Bermúdez ◽  
Dayana Calderon ◽  
Armando Moreno-Vranich ◽  
Hannia Almonacid ◽  
Manuel A. Patarroyo ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Massimiliano Francesco Peana ◽  
Serenella Medici ◽  
Alessia Ledda ◽  
Valeria Marina Nurchi ◽  
Maria Antonietta Zoroddu

P1D2E3K4H5E6L7(PK9-H), a fragment of Ypk9, the yeast homologue of the human Park9 protein, was studied for its coordination abilities towards Ni(II) and Cu(II) ions through mono- and bi-dimensional NMR techniques. Both proteins are involved in the transportation of metal ions, including manganese and nickel, from the cytosol to the lysosomal lumen. Ypk9 showed manganese detoxification role, preventing a Mn-induced Parkinsonism (PD) besides mutations in Park9, linked to a juvenile form of the disease. Here, we tested PK9-H with Cu(II) and Ni(II) ions, the former because it is an essential element ubiquitous in the human body, so its trafficking should be strictly regulated and one cannot exclude that Ypk9 may play a role in it, and the latter because, besides being a toxic element for many organisms and involved in different pathologies and inflammation states, it seems that the protein confers protection against it. NMR experiments showed that both cations can bind PK9-H in an effective way, leading to complexes whose coordination mode depends on the pH of the solution. NMR data have been used to build a model for the structure of the major Cu(II) and Ni(II) complexes. Structural changes in the conformation of the peptide with organized side chain orientation promoted by nickel coordination were detected.


2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chih-Hao Lu ◽  
Chin-Sheng Yu ◽  
Yu-Tung Chien ◽  
Shao-Wei Huang

We propose a method (EXIA2) of catalytic residue prediction based on protein structure without needing homology information. The method is based on the special side chain orientation of catalytic residues. We found that the side chain of catalytic residues usually points to the center of the catalytic site. The special orientation is usually observed in catalytic residues but not in noncatalytic residues, which usually have random side chain orientation. The method is shown to be the most accurate catalytic residue prediction method currently when combined with PSI-Blast sequence conservation. It performs better than other competing methods on several benchmark datasets that include over 1,200 enzyme structures. The areas under the ROC curve (AUC) on these benchmark datasets are in the range from 0.934 to 0.968.


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