On-Axis Alignment of Protein Nanocage Assemblies from 2D to 3D through the Aromatic Stacking Interactions of Amino Acid Residues

ACS Nano ◽  
2018 ◽  
Vol 12 (11) ◽  
pp. 11323-11332 ◽  
Author(s):  
Kai Zhou ◽  
Jiachen Zang ◽  
Hai Chen ◽  
Wenming Wang ◽  
Hongfei Wang ◽  
...  
Keyword(s):  
Amino Acid ◽  
Stacking Interactions ◽  
Amino Acid Residues ◽  
Aromatic Stacking ◽  
Axis Alignment ◽  
2D To 3D

Substrate Recognition by Vitamin K-Dependent Carboxylase

1987 ◽  
Vol 57 (01) ◽  
pp. 017-019 ◽  
Author(s):  
Magda M W Ulrich ◽  
Berry A M Soute ◽  
L Johan M van Haarlem ◽  
Cees Vermeer
Keyword(s):  
Amino Acid ◽  
Vitamin K ◽  
Substrate Recognition ◽  
Bovine Liver ◽  
Amino Acid Residues

SummaryDecarboxylated osteocalcins were prepared and purified from bovine, chicken, human and monkey bones and assayed for their ability to serve as a substrate for vitamin K-dependent carboxylase from bovine liver. Substantial differences were observed, especially between bovine and monkey d-osteocalcin. Since these substrates differ only in their amino acid residues 3 and 4, it seems that these residues play a role in the recognition of a substrate by hepatic carboxylase.

Statistical Force-Field for Structural Modeling Using Chemical Cross-Linking/mass Spectrometry Distance Constraints

2018 ◽  
Author(s):  
Allan J. R. Ferrari ◽  
Fabio C. Gozzo ◽  
Leandro Martinez
Keyword(s):  
Mass Spectrometry ◽  
Amino Acid ◽  
Force Field ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Structural Modeling ◽  
Cross Linking ◽  
Amino Acid Residues ◽  
Distance Constraints ◽  
Chemical Cross Linking

<div><p>Chemical cross-linking/Mass Spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues, which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms. Here, a force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. The force-field can be easily incorporated into current modeling methods and software. In this work, the force-field was implemented within the Rosetta ab initio relax protocol. We show a significant improvement in the quality of the models obtained relative to current strategies for constraint representation. This force-field contributes to the long-desired goal of obtaining the tertiary structures of proteins using XLMS data. Force-field parameters and usage instructions are freely available at http://m3g.iqm.unicamp.br/topolink/xlff <br></p></div><p></p><p></p>

A Chemical Perspective to the Anti-Amyloid Action of Compounds and a Nanoparticle Based Assay for Screening Amyloid Inhibitors

2020 ◽  
Author(s):  
Nidhi Gour ◽  
Bharti Koshti
Keyword(s):  
Memory Loss ◽  
Cost Effective ◽  
Structural Motif ◽  
Rapid Screening ◽  
Stacking Interactions ◽  
Aromatic Rings ◽  
Amyloid Fibers ◽  
Aromatic Stacking ◽  
Cost Effective Technique ◽  
The Brain

Aggregation of amyloid beeta 1-42 (Aβ<sub>42</sub>) peptide causes the formation of clustered deposits knows as amyloid plaques in the brain which leads to neuronal dysfunction and memory loss and associated with many neurological disorders including Alzheimer’s and Parkinson’s. Aβ<sub>42</sub> has core structural motif with phenylalanine at the 19 and 20 positions. The diphenylalanine (FF) residue plays a crucial role in the formation of amyloid fibers and serves as model peptide for studying Aβ<sub>42 </sub>aggregation. FF self-assembles to well-ordered tubular morphology via aromatic pi-pi stackings. Our studies, suggest that the aromatic rings present in the anti-amyloidogenic compounds may interact with the pi-pi stacking interactions present in the FF. Even the compounds which do not have aromatic rings, like cyclodextrin and cucurbituril show anti-amyloid property due to the binding of aromatic ring inside the guest cavity. Hence, our studies also suggest that compounds which may have a functional moiety capable of interacting with the aromatic stacking interactions might be tested for their anti-amyloidogenic properties. Further, in this manuscript, we have proposed two novel nanoparticle based assays for the rapid screening of amyloid inhibitors. In the first assay, interaction between biotin-tagged FF peptide and the streptavidin labelled gold nanoparticles (s-AuNPs) were used. In another assay, thiol-Au interactions were used to develop an assay for detection of amyloid inhibitors. It is envisaged that the proposed analytical method will provide a simple, facile and cost effective technique for the screening of amyloid inhibitors and may be of immense practical implications to find the therapeutic remedies for the diseases associated with the protein aggregation.

Predicting the Strength of Stacking Interactions Between Heterocycles and Aromatic Amino Acid Side Chains

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Analise C. Doney ◽  
Steven Wheeler
Keyword(s):  
Amino Acid ◽  
Binding Sites ◽  
Aromatic Amino Acid ◽  
Aromatic Amino Acids ◽  
Biologically Active ◽  
Side Chains ◽  
Stacking Interactions ◽  
Inhibitor Binding ◽  
Protein Binding Sites ◽  
Amino Acid Side Chains

<p>Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. Based on rigorous <i>ab initio</i> data, we have devised a simple predictive model of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. This model provides rapid predictions of the stacking ability of a given heterocycle based on readily-computed heterocycle descriptors. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow simple predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual model for understanding stacking interactions in protein binding sites and optimizing inhibitor binding in drug design.</p>

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