scholarly journals Surface-based protein binding pocket similarity

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

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.


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.


2019 ◽  
Vol 17 (5) ◽  
pp. 1076-1080 ◽  
Author(s):  
Bryan J. Lampkin ◽  
Cecilia Monteiro ◽  
Evan T. Powers ◽  
Paige M. Bouc ◽  
Jeffery W. Kelly ◽  
...  

Specific interactions between a protein and fluorophore are essential to realize strong ratiometric differences in emission wavelength at protein–protein interfaces.


2021 ◽  
Author(s):  
Ke Wen ◽  
Zhuo Wang ◽  
Tao Chen ◽  
Hua Liu ◽  
Yahu Liu ◽  
...  

Abstract Artificial tubular molecular pockets bearing polar functionalities on their inner surface are useful model systems for understanding the mechanisms of protein-ligand interactions in living systems. We herein report a pillar[5]arene-derived molecular tube, [P4-(OH)BPO], whose endo conformational isomer endo-[P4-(OH)BPO] possesses an inwardly pointing hydrogen-bond (H-bond) donor (OH) in its deep cavity, a strong H-bond acceptor (C=O) on the predominantly hydrophobic inner surface, rendering it a perfect protein binding pocket mimetic. By measuring the binding affinity of this pocket-mimetic tube, we screened a library of various shape-complementary organic guests (1–38) resembling the fragment ligands in fragment-based drug design (FBDD). On the basis of the data for “fragment-pocket” complexes (1–38)⊂endo-[P4-(OH)BPO], two rationally designed “lead molecules” (39 and 40) were identified to be able to enhance binding affinity significantly by forming H-bonds with both the donor and acceptor of endo-[P4-(OH)BPO]. The described work opens new avenues for developing pillar[n]arene-derived protein binding pocket-mimetic systems for studies on protein-ligand interactions and mechanisms of enzymatic reactions.


2018 ◽  
Author(s):  
Bryan J. Lampkin ◽  
Cecilia Monteiro ◽  
Evan T. Powers ◽  
Paige M. Bouc ◽  
Jeffery W. Kelly ◽  
...  

<p>ESIPT involves a photochemical isomerization and creates the opportunity for the emission of two distinct wavelengths of light from a single fluorophore. The selectivity between these two wavelengths of emission is dependent on the environment around the fluorophore and suggests the possibility for ratiometric monitoring of protein microenvironments. Unfortunately, nonspecific binding of ESIPT fluorophores does not often lead to dramatic changes in the ratio between the two wavelengths of emission. A protein binding pocket was designed to selectively discriminate between the two channels of emission available to an ESIPT fluorophore. More broadly, this work demonstrates that specific interactions between the protein and the fluorophore are essential to realize strong ratiometric differences between the two possible wavelengths of emission. The design strategies proposed here lead to an ESIPT fluorophore that can discern subtle differences in the interface between two proteins.</p>


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