Protein structure refinement using a quantum mechanics-based chemical shielding predictor

Bratholm, Lars Andersen; Jensen, Jan H.

doi:10.1039/c6sc04344e

Cited by 9 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The root‐mean‐squared deviation (RMSD) is a widely used measure of distance between two aligned objects. [ 45 ] The smaller RMSD value, the better docking results. Therefore, the RMSD values of the three probes were calculated using PyMOL software.…”

Section: Resultsmentioning

confidence: 99%

Near‐Infrared Bodipy‐Based Molecular Rotors for β‐Amyloid Imaging In Vivo

Geng

Zhang

et al. 2023

Adv Healthcare Materials

View full text Add to dashboard Cite

Aβ is one of the important biomarkers for diagnosing Alzheimer's disease. Many near‐infrared (NIR) probes based on the donor‐π‐accepter structure have been developed to detect Aβ. Most reported Aβ probes are based on the N,N‐dimethylamino group as the ideal donor, which is one of the widely accepted binding units. The development of new fluorescent probes with new binding units to detect Aβ are urgently required. Therefore, we developed three anchoring molecular rotor electron donors consisting of cyclic amines of different ring sizes of, namely five‐membered ring (TPyr), six‐membered ring (TPip), and seven‐membered ring (THAI). These new anchored molecular rotors were connected to a BODIPY and named TPyrBDP, TPipBDP, and THAIBDP. These probes exhibited high affinities (from 28 nM to 54 nM) for Aβ1‐42 aggregates. The six‐membered ring dye TPipBDP exhibited the highest signal‐to‐noise (75.5‐fold) and higher affinity (28.30 ± 5.94 nM). TPipBDP can cross the blood‐brain barrier (BBB) and exhibits higher fluorescence enhancement with Tg mice than with wild‐type mice.This article is protected by copyright. All rights reserved

show abstract

Section: Resultsmentioning

confidence: 99%

Near‐Infrared Bodipy‐Based Molecular Rotors for β‐Amyloid Imaging In Vivo

Geng

Zhang

et al. 2023

Adv Healthcare Materials

View full text Add to dashboard Cite

show abstract

“…Each docked structure was scored by the built-in scoring function and was clustered by 0.8 Å of RMSD criteria. 28 For each binding model, a molecular mechanics/Poisson–Boltzmann surface area analysis was performed. Before these calculations were made, the complex structure was further refined initially with the steepest descent algorithm and then the conjugated gradient algorithm using the Amber9 package.…”

Section: Methodsmentioning

confidence: 99%

Binding studies between cytosinpeptidemycin and the superfamily 1 helicase protein of tobacco mosaic virus

Chen

Gao

et al. 2018

RSC Adv.

View full text Add to dashboard Cite

show abstract

“…CS have been implemented as restraints in different types of simulations including MD [ 16 ], MC [ 40 ], simulated annealing [ 17 ]), or integrated in search and select programs (CS-Rosetta [ 41 ], ENSEMBLE [ 28 ], and MESMER [ 19 ]). In addition, CS data have proven very useful to refine structures or to make new structural models [ 42 ].…”

Section: Nuclear Magnetic Resonancementioning

confidence: 99%

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

2020

View full text Add to dashboard Cite

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

show abstract

Protein structure refinement using a quantum mechanics-based chemical shielding predictor

Abstract: We show that a QM-based predictor of a protein backbone and CB chemical shifts is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors (errors in chemical shifts shown in red).

Cited by 9 publications

References 40 publications

Near‐Infrared Bodipy‐Based Molecular Rotors for β‐Amyloid Imaging In Vivo

Near‐Infrared Bodipy‐Based Molecular Rotors for β‐Amyloid Imaging In Vivo

Binding studies between cytosinpeptidemycin and the superfamily 1 helicase protein of tobacco mosaic virus

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Contact Info

Product

Resources

About