Recognition of Potential COVID-19 Drug Treatments Through the Study of Existing Protein-Drug and Protein-Protein Structures: An Analysis of Kinetically Active Residues - Ivermectin protein binding properties (2020-08-11)
Abstract: "We report the results of our study of approved drugs as potential treatments for COVID 19, based on the application of various bioinformatics predictive methods. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). Our results indicate that these small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general."
They used a bunch of AI and algorithms with 3D crystal structure imaging in this study to identify drugs that target specific regions on viral and human proteins, one of which is ivermectin. Targeting these kinetic regions could be very beneficial to being able to directly target SARS-CoV-2. Studying the folding proteins is a very complex process so this is only looking at the outer protien structures and binding mechanisms. They propose further research of with Deep Mind and neural net learning to analyze the internal folding protein structures and the drug interaction modeling etc.. Lots of computer power needed basically. They explain their super computer stuff at the end, Im just an amateur Ivermectin/Covid researcher so its all a bit over my head
"The drug ivermectin binds glutamate-gated chloride channels and thus increases their permeability to chloride ions. We analyzed the ivermectin’s binding to the human glycine receptor alpha-3 (pdb id 5vdh [56]). This structure, besides ivermectin, also has glycine and the potentiator AM-3607 (7c6) bound to the glycine receptor. The comparison of the crystal structure used in this research to previously determined structures revealed that the ivermectin binding expands the ion channel pore [56]"
The analysis (Figure 2) reveals that all three compounds (ivermectin, glycine and AM-3607 (7c6)) bind to kinetically active and adjoining residues [35], some of which are highly hydrophobic, with ivermectin binding almost exclusively hydrophobic residues. That means that this drug well seek similar sites on the surface of the Covid-19 proteins."
These drugs can reduce structural entropy (destroy the virus) but to fully know how much they actually can destroy the structure of the virus they need a much more robust computational model with help from AI and neural net learning:
"Our results indicate that small, drug like compounds preferentially bind to kinetically active and adjoining residues, thus seeking stable residues characterized by fast normal modes with small amplitude of fluctuations [35]. Some drugs preferentially seek active patches that are hydrophobic (chloroquine, ivermectin), while others prefer hydrophilic surfaces (remdesivir, sofosbuvir, eflornithine). We can postulate that in water environment drugs binding to hydrophilic patches will be more stable, as their removal will lead toward the reduction in structural entropy, but a full account of this proposition will require calculations of binding free energy differences based on full atom molecular dynamics, using, for instance, steered molecular dynamics simulations (SMD) [64-66]. We can also propose that the drugs/small molecules that bind to deep pockets will be more stable, and thus more effective. Our algorithm accurately recognizes such pockets as binding spots for drugs (Figures 1a, 3 and 10), and small peptides (see, in particular, Figure 6a in [35]).
More effective drugs combinations:
"Multidrug cocktails are frequently used to treat viral diseases [67]. Our analysis shows that in designing antiviral drug cocktails, binding affinity between drugs and kinetically active (stable) sites should be combined with the information on their hydrophobic and hydrophilic properties in attempt to avoid binding competition, increase drug cocktail efficiency, and reduce toxicity and other unwanted side effects.In our analysis we used both viral-parasitic, as well as human proteins. The analysis shows that kinetically active residues exist in both human and non-human proteins/enzymes and that drugs bind indiscriminately to them regardless of their origin. The compounds that bind to human proteins potentially offer longer lasting treatments as host cells and tissues have less chance of developing drug resistance through single point mutations."
[-] foggynotion | 1 points | Aug 22 2020 20:13:58
Abstract: "We report the results of our study of approved drugs as potential treatments for COVID 19, based on the application of various bioinformatics predictive methods. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). Our results indicate that these small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general."
They used a bunch of AI and algorithms with 3D crystal structure imaging in this study to identify drugs that target specific regions on viral and human proteins, one of which is ivermectin. Targeting these kinetic regions could be very beneficial to being able to directly target SARS-CoV-2. Studying the folding proteins is a very complex process so this is only looking at the outer protien structures and binding mechanisms. They propose further research of with Deep Mind and neural net learning to analyze the internal folding protein structures and the drug interaction modeling etc.. Lots of computer power needed basically. They explain their super computer stuff at the end, Im just an amateur Ivermectin/Covid researcher so its all a bit over my head
"The drug ivermectin binds glutamate-gated chloride channels and thus increases their permeability to chloride ions. We analyzed the ivermectin’s binding to the human glycine receptor alpha-3 (pdb id 5vdh [56]). This structure, besides ivermectin, also has glycine and the potentiator AM-3607 (7c6) bound to the glycine receptor. The comparison of the crystal structure used in this research to previously determined structures revealed that the ivermectin binding expands the ion channel pore [56]"
The analysis (Figure 2) reveals that all three compounds (ivermectin, glycine and AM-3607 (7c6)) bind to kinetically active and adjoining residues [35], some of which are highly hydrophobic, with ivermectin binding almost exclusively hydrophobic residues. That means that this drug well seek similar sites on the surface of the Covid-19 proteins."
These drugs can reduce structural entropy (destroy the virus) but to fully know how much they actually can destroy the structure of the virus they need a much more robust computational model with help from AI and neural net learning:
"Our results indicate that small, drug like compounds preferentially bind to kinetically active and adjoining residues, thus seeking stable residues characterized by fast normal modes with small amplitude of fluctuations [35]. Some drugs preferentially seek active patches that are hydrophobic (chloroquine, ivermectin), while others prefer hydrophilic surfaces (remdesivir, sofosbuvir, eflornithine). We can postulate that in water environment drugs binding to hydrophilic patches will be more stable, as their removal will lead toward the reduction in structural entropy, but a full account of this proposition will require calculations of binding free energy differences based on full atom molecular dynamics, using, for instance, steered molecular dynamics simulations (SMD) [64-66]. We can also propose that the drugs/small molecules that bind to deep pockets will be more stable, and thus more effective. Our algorithm accurately recognizes such pockets as binding spots for drugs (Figures 1a, 3 and 10), and small peptides (see, in particular, Figure 6a in [35]).
More effective drugs combinations:
"Multidrug cocktails are frequently used to treat viral diseases [67]. Our analysis shows that in designing antiviral drug cocktails, binding affinity between drugs and kinetically active (stable) sites should be combined with the information on their hydrophobic and hydrophilic properties in attempt to avoid binding competition, increase drug cocktail efficiency, and reduce toxicity and other unwanted side effects. In our analysis we used both viral-parasitic, as well as human proteins. The analysis shows that kinetically active residues exist in both human and non-human proteins/enzymes and that drugs bind indiscriminately to them regardless of their origin. The compounds that bind to human proteins potentially offer longer lasting treatments as host cells and tissues have less chance of developing drug resistance through single point mutations."
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