Computational analysis of altered one- and two-photon CD of sterols inside a protein binding pocket

2022 ◽  
Vol 141 (1) ◽  
Author(s):  
Salvatore Prioli ◽  
Daniel Wüstner ◽  
Jacob Kongsted
2011 ◽  
Vol 12 (1) ◽  
pp. 62 ◽  
Author(s):  
Jonathon T Hill ◽  
Keith R Anderson ◽  
Teresa L Mastracci ◽  
Klaus H Kaestner ◽  
Lori Sussel

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Ting Ran ◽  
Hongming Chen

<p><i>De novo</i> molecule design through molecular generative model is gaining increasing attention in recent years. Here a novel generative model was proposed by integrating the 3D structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of protein binding pocket is effectively characterized through a coarse-grain strategy and the three-dimensional information of the pocket can be represented by the sorted eigenvalues of the coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor DeeplyTough to train cRNN models and compared their performance. It has been shown that the molecules generated with the control of protein environment information have a clear tendency on generating compounds with higher similarity to the original X-ray bound ligand than normal RNN model and also achieving better performance in terms of docking scores. Our results demonstrate the potential application of EGCM controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space. </p><p> </p>


ACS Omega ◽  
2020 ◽  
Vol 5 (24) ◽  
pp. 14297-14307 ◽  
Author(s):  
Dimitris Gazgalis ◽  
Mehreen Zaka ◽  
Bilal Haider Abbasi ◽  
Diomedes E. Logothetis ◽  
Mihaly Mezei ◽  
...  

2011 ◽  
Vol 79 (9) ◽  
pp. 2746-2763 ◽  
Author(s):  
Russell Spitzer ◽  
Ann E. Cleves ◽  
Ajay N. Jain

2018 ◽  
Vol 115 (12) ◽  
pp. 3036-3041 ◽  
Author(s):  
Yinglong Miao ◽  
J. Andrew McCammon

Protein–protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein–protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M2 muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M2 receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR–nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein–protein interactions.


2010 ◽  
Vol 50 (10) ◽  
pp. 1759-1771 ◽  
Author(s):  
Gene M. Ko ◽  
A. Srinivas Reddy ◽  
Sunil Kumar ◽  
Barbara A. Bailey ◽  
Rajni Garg

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Ting Ran ◽  
Hongming Chen

<p><i>De novo</i> molecule design through molecular generative model is gaining increasing attention in recent years. Here a novel generative model was proposed by integrating the 3D structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of protein binding pocket is effectively characterized through a coarse-grain strategy and the three-dimensional information of the pocket can be represented by the sorted eigenvalues of the coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor DeeplyTough to train cRNN models and compared their performance. It has been shown that the molecules generated with the control of protein environment information have a clear tendency on generating compounds with higher similarity to the original X-ray bound ligand than normal RNN model and also achieving better performance in terms of docking scores. Our results demonstrate the potential application of EGCM controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space. </p><p> </p>


2019 ◽  
Vol 17 (5) ◽  
pp. 1081-1089 ◽  
Author(s):  
Rohit Kumar ◽  
Kristoffer Peterson ◽  
Majda Misini Ignjatović ◽  
Hakon Leffler ◽  
Ulf Ryde ◽  
...  

Analysis of a ligand induced-aglycone-binding pocket in galectin-3 provides detailed insight into interactions of fluorinated phenyl moieties with arginine-containing protein binding sites and the complex interplay of different energetic components in defining the binding affinity.


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