Understanding Docking Complexes of Macromolecules Using HADDOCK: The Synergy between Experimental Data and Computations

Saponaro, Andrea; Maione, Vincenzo; Bonvin, Alexandre; Cantini, Francesca

doi:10.21769/bioprotoc.3793

Cited by 13 publications

(3 citation statements)

References 13 publications

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“…For protein-protein docking, HSP40, HSP70, and HSP90 were docked with SARS-CoV-2 NSP2 using ClusPro (https://bioinfo3d.cs.tau.ac.il) [14,15]. ClusPro allows the input of two modelled structures, the receptor and the ligand, and no parameters are set manually.…”

Section: Molecular Docking and Analysismentioning

confidence: 99%

In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development

Yamkela,

Sitobo,

Makhoba

2023

CIMB

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The non-structural protein 2 (NSP2) is an RNA-binding protein involved in coronavirus genome replication, and it often decreases human immune response to promote viral invasion and development. It is believed that the NSP2 associates itself with polyamines and heat shock proteins inside the host cell to proceed with viral development. This study aimed to investigate how the SARS-CoV-2 virus’ key non-structural proteins (NSP2) utilize polyamines and heat shock proteins using a molecular docking approach and molecular dynamics (MD). ClusPro and HADDOCK servers were used for the docking and Discovery Studio, chimera, and PyMOL were used for analysis. Docking of the heat shock proteins 40 (HSP40), 70 (HSP70), and 90 (HSP90) with SARS-CoV-2 NSP2 resulted in 32, 28, and 19 interactions, respectively. Molecular dynamics revealed Arg458, Asn508, Met297, Arg301, and Trp417 as active residues, and pharmacophore modeling indicated ZINC395648, ZINC01150525, and ZINC85324008 from the zinc database as possible inhibitors for this NSP2.

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Section: Molecular Docking and Analysismentioning

confidence: 99%

In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development

Yamkela,

Sitobo,

Makhoba

2023

CIMB

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“…These final water-refined models are clustered and ranked based on their respective HADDOCK score. This clustering is done based on pairwise Root Mean Square Deviations (RMSD), with the default value of RMSD being set to a cutoff of 7.5 Å, and the minimum number of structures to define a cluster is four (by default) (Saponaro et al, 2020). Detailed statistics that represent the average values of the HADDOCK score along with other standard energies including Van der Waals, Desolvation energy, Restraint violation energy, Intermolecular binding energy, non-bonded interaction energy, buried surface area, and Z-score were calculated for the high scoring complexes in each cluster.…”

Section: Intra-viral and Inter-viral Protein-protein Interactions (Ppi)mentioning

confidence: 99%

In silico evaluation of molecular virus–virus interactions taking place between Cotton leaf curl Kokhran virus- Burewala strain and Tomato leaf curl New Delhi virus

Ali

Paracha

Tahir

2021

PeerJ

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Background Cotton leaf curl disease (CLCuD) is a disease of cotton caused by begomoviruses, leading to a drastic loss in the annual yield of the crop. Pakistan has suffered two epidemics of this disease leading to the loss of billions in annual exports. The speculation that a third epidemic of CLCuD may result as consequence of the frequent occurrence of Tomato leaf curl New Delhi virus (ToLCNDV) and Cotton leaf curl Kokhran Virus-Burewala Strain (CLCuKoV-Bu) in CLCuD infected samples, demand that the interactions taking between the two viruses be properly evaluated. This study is designed to assess virus-virus interactions at the molecular level and determine the type of co-infection taking place. Methods Based on the amino acid sequences of the gene products of both CLCuKoV-Bu and ToLCNDV, protein structures were generated using different software, i.e., MODELLER, I-TASSER, QUARKS, LOMETS and RAPTORX. A consensus model for each protein was selected after model quality assessment using ERRAT, QMEANDisCo, PROCHECK Z-Score and Ramachandran plot analysis. The active and passive residues in the protein structures were identified using the CPORT server. Protein–Protein Docking was done using the HADDOCK webserver, and 169 Protein–Protein Interaction (PPIs) were performed between the proteins of the two viruses. The docked complexes were submitted to the PRODIGY server to identify the interacting residues between the complexes. The strongest interactions were determined based on the HADDOCK Score, Desolvation energy, Van der Waals Energy, Restraint Violation Energy, Electrostatic Energy, Buried Surface Area and Restraint Violation Energy, Binding Affinity and Dissociation constant (Kd). A total of 50 ns Molecular Dynamic simulations were performed on complexes that exhibited the strongest affinity in order to validate the stability of the complexes, and to remove any steric hindrances that may exist within the structures. Results Our results indicate significant interactions taking place between the proteins of the two viruses. Out of all the interactions, the strongest were observed between the Replication Initiation protein (Rep) of CLCuKoV-Bu with the Movement protein (MP), Nuclear Shuttle Protein (NSP) of ToLCNDV (DNA-B), while the weakest were seen between the Replication Enhancer protein (REn) of CLCuKoV-Bu with the REn protein of ToLCNDV. The residues identified to be taking a part in interaction belonged to domains having a pivotal role in the viral life cycle and pathogenicity. It maybe deduced that the two viruses exhibit antagonistic behavior towards each other, and the type of infection may be categorised as a type of Super Infection Exclusion (SIE) or homologous interference. However, further experimentation, in the form of transient expression analysis, is needed to confirm the nature of these interactions and increase our understanding of the direct interactions taking place between two viruses.

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“…Over the last years, computational methods to study macromolecular interactions have been steadily incorporating different types of data to guide, filter, or validate their predictions [7][8][9][10][11][12][13]. The use of various types of information in macromolecular docking is commonly referred to as integrative modeling and has been a convergence point in the field, being implemented in most software under active development [14].…”

Section: Introductionmentioning

confidence: 99%

Information-driven modeling of biomolecular complexes

Noort

Honorato

Bonvin

2021

Current Opinion in Structural Biology

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Understanding Docking Complexes of Macromolecules Using HADDOCK: The Synergy between Experimental Data and Computations

Cited by 13 publications

References 13 publications

In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development

In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development

In silico evaluation of molecular virus–virus interactions taking place between Cotton leaf curl Kokhran virus- Burewala strain and Tomato leaf curl New Delhi virus

Information-driven modeling of biomolecular complexes

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