Identification of probable inhibitors for the DNA polymerase of the Monkeypox virus through the virtual screening approach

Kumari, Swati; Chakraborty, Sanjoy; Ahmad, Mohammed; Tailor, Prafullakumar; Biswal, B.K.

doi:10.1016/j.ijbiomac.2022.12.252

Cited by 17 publications

(11 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…None of these genomes showed mutations in ID_6924 at residues that were previously identified to directly interact with tecovirimat ( 37 ). On the contrary, mutations at two brincidofovir-interacting positions ( 38 ) within ID_8713 were detected, namely R628K (occurring in one genome sequence sampled in July 2022) and N785D (occurring in six genome sequences, with one sampled in May 2022 and five sampled from 2006 to 2007). A time-resolved analysis of mutations in the drug targets ID_6924 and ID_8713 showed that the majority of mutation events occurred from June 2022 to October 2022, which are certainly coupled with the increased number of total genomes sampled during that time and thus underline the importance of extensive genome sequencing for the identification of potentially risk-associated mutations in human pathogens like MPX ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Cα-atoms of mutated positions detected before the 2022 MPX outbreak are shown as spheres, colored with the PyMOL “rainbow” palette (minimum = 0, maximum = 100) by the mutations’ frequency (labeled in %) in all genome samples where the respective protein sequence was identified. Residues previously predicted to interact with tecovirimat ( 37 ) (left) and brincidofovir ( 38 ) (right) are shown as black lines, and the respective binding site cavity is colored by the electrostatics of its surroundings (blue-white-red spectrum ranging from −1 to +1). ( B ) Structural representation as in (A), showing mutated positions detected within 1 year after the 2022 MPX outbreak.…”

Section: Resultsmentioning

confidence: 99%

“…At the time this manuscript was submitted, experimentally determined complex structures of protein-ligand complexes were unavailable for the specific protein-drug combinations which are proposed for the treatment of monkeypox. We, therefore, considered the drug-interacting residues presented in previous studies for tecovirimat ( 37 ) and brincidofovir ( 38 ) and calculated point clouds representing the cavities at the respective proposed binding sites. We like to emphasize that the Catalophore cavity matching approach has been validated and published, demonstrating that similar binding site cavities in two distinct proteins correspond to their ability to bind the same molecule ( 40 ).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak

Parigger,

Krassnigg,

Grabuschnig

et al. 2023

Microbiol Spectr

View full text Add to dashboard Cite

The monkeypox virus (MPX) belongs to the Orthopoxvirus genus of the Poxviridae family, is endemic in parts of Africa and causes a disease in humans similar to smallpox. The most recent outbreak of MPX is already affecting 110 countries, with 86,956 confirmed cases since May 2022 and has consequently become a focus of interest. In particular, a molecular understanding of the virus is essential to study infection processes and pathogen-host interactions, predict tropism changes, or guide drug development and drug discovery as well as vaccine development or vaccine adaptation at a very early stage. Herein, we present a study of the structural proteome of the currently circulating MPX: Our consensus analysis of 3,713 genome sequences sampled within a year after the outbreak revealed 10,580 characteristic candidate open reading frames (ORFs). A search in the non-redundant protein database reduced the number of suspected ORFs to 1,079, of which 210 are representative proteins in typical MPX reference genomes. This should serve as a collection of putative proteins within the currently spreading MPX, a compound of information that could support timely drug discovery, mutational analyses, and vaccine development. We, herein, present the so far most comprehensive structural proteome by providing atomistic 3D models of 210 proteins, generated with three state-of-the-art structure prediction methods, including a mutational analysis of the proteome, with a particular focus on the drug-binding sites of tecovirimat and brincidofovir. IMPORTANCE The 2022 outbreak of the monkeypox virus already involves, by April 2023, 110 countries with 86,956 confirmed cases and 119 deaths. Understanding an emerging disease on a molecular level is essential to study infection processes and eventually guide drug discovery at an early stage. To support this, we provide the so far most comprehensive structural proteome of the monkeypox virus, which includes 210 structural models, each computed with three state-of-the-art structure prediction methods. Instead of building on a single-genome sequence, we generated our models from a consensus of 3,713 high-quality genome sequences sampled from patients within 1 year of the outbreak. Therefore, we present an average structural proteome of the currently isolated viruses, including mutational analyses with a special focus on drug-binding sites. Continuing dynamic mutation monitoring within the structural proteome presented here is essential to timely predict possible physiological changes in the evolving virus.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak

Parigger,

Krassnigg,

Grabuschnig

et al. 2023

Microbiol Spectr

View full text Add to dashboard Cite

show abstract

“…According to this rule, a compound must possess the following properties: molecular weight < 500 Da, logP < 5, H-bond donors < 5, and H-bond acceptors < 10. To further select drug-like molecules, the prepared library was filtered to remove any molecule that violated Lipinski's rule of five [50,51]. The initial curation resulted in a library of 18,113 ligands.…”

Section: Dataset Selection and Curationmentioning

confidence: 99%

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Baselious,

Hilscher,

Robaa

et al. 2024

IJMS

View full text Add to dashboard Cite

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

show abstract

“…AlphaFold is widely used in research on other eukaryotic viruses, including monkeypox virus (MPXV) [ 31 , 32 , 33 , 34 ], herpes simplex virus [ 35 , 36 ], hepatitis E virus (HEV) [ 37 ] and other viral pathogens of humans and economically significant animals and plants [ 38 , 39 , 40 , 41 , 42 , 43 ]. Monkeypox virus (MPXV) represents a new serious threat to human health.…”

Section: Application Of Af2 For Research On Eukaryotic Virusesmentioning

confidence: 99%

Using AlphaFold Predictions in Viral Research

Gutnik

Evseev

Miroshnikov

et al. 2023

CIMB

View full text Add to dashboard Cite

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

show abstract

Identification of probable inhibitors for the DNA polymerase of the Monkeypox virus through the virtual screening approach

Cited by 17 publications

References 63 publications

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Using AlphaFold Predictions in Viral Research

Contact Info

Product

Resources

About