scholarly journals Finding Druggable Sites in Proteins using TACTICS

2021 ◽  
Author(s):  
Daniel J. Evans ◽  
Remy A. Yovanno ◽  
Sanim Rahman ◽  
David W. Cao ◽  
Morgan Q. Beckett ◽  
...  
Keyword(s):  
Binding Sites ◽  
Learning Algorithm ◽  
Drug Binding ◽  
Molecular Structures ◽  
Dynamics Simulation ◽  
Conformational Heterogeneity ◽  
Main Protease ◽  
Binding Pockets ◽  
Drug Binding Sites ◽  
Fragment Docking

AbstractStructure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (Trajectory-based Analysis of Conformations To Identify Cryptic Sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Teruhisa S. Komatsu ◽  
Noriaki Okimoto ◽  
Yohei M. Koyama ◽  
Yoshinori Hirano ◽  
Gentaro Morimoto ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Main Protease ◽  
Encounter Complex ◽  
Dynamic Sampling

Abstract We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein–ligand complexes and suggest the possibilities of further drug optimisations.

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Systematic exploration of multiple drug binding sites

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Mónika Bálint ◽  
Norbert Jeszenői ◽  
István Horváth ◽  
David van der Spoel ◽  
Csaba Hetényi
Keyword(s):  
Binding Sites ◽  
Drug Binding ◽  
Multiple Drug ◽  
Systematic Exploration ◽  
Drug Binding Sites

scholarly journals Footprinting studies on the effect of echinomycin on the structure of a bent DNA fragment

1990 ◽  
Vol 269 (1) ◽  
pp. 217-221 ◽  
Author(s):  
K R Fox ◽  
E Kentebe
Keyword(s):  
Binding Sites ◽  
Dna Structure ◽  
Drug Binding ◽  
Micrococcal Nuclease ◽  
Kinetoplast Dna ◽  
Bent Dna ◽  
Diethyl Pyrocarbonate ◽  
Drug Binding Sites ◽  
Dna Fragment

The interaction of echinomycin with a kinetoplast DNA fragment which contains phased runs of adenine residues has been examined by various footprinting techniques. DNAase I footprinting confirms that all drug-binding sites contain the dinucleotide CpG. However, not all such sequences are protected. Three sites, each of which is located between two adenine tracks in the sequence GCGA, are not protected from DNAase I attack. Enhanced cleavage by DNAase I, DNAase II and micrococcal nuclease is observed in regions surrounding drug-binding sites. The results suggest that echinomycin alters the conformation of the AT tracks, making them more like an average DNA structure. Echinomycin renders adenine residues in the sequence CGA hyper-reactive to diethyl pyrocarbonate.

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