Dynamics and binding interactions of peptide inhibitors of dengue virus entry

Isa, Diyana Mohd; Chin, Sek Peng; Chong, Wei Lim; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.; Lee, Vannajan Sanghiran

doi:10.1007/s10867-018-9515-6

Cited by 20 publications

(13 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Docking, however, still faces difficulties, particularly regarding the correct modeling of ligand and protein flexibility [15][16][17][18] and of water-mediated interactions [18,19]. It is widely used for small molecules, but its use for small peptides and other larger biomolecules has only been under development in the last decade [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

View full text Add to dashboard Cite

AutoDock and Vina are two of the most widely used protein-ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.of the poses previously created, discriminating between the best and not so good alternatives [1,5]. This estimate, which in some cases is a prediction of the free energy of binding, must be able to discriminate between molecules that bind to the target and those that do not [23]. When looking at the two enantiomers, for example, it is still not possible to identify the most active form with most of the scoring functions used by the most common docking software [24].Even with all the significant improvements in computational power and docking software, considering all interactions that happen when a ligand binds to its target is an extremely challenging task. In order to be rigorous, the scoring functions would have to be much more complex, involve quantum calculations and, thus, these assays would turn out to be considerably expensive and time-consuming. When applying a virtual screening protocol, one wishes to screen very large databases of compounds in a relatively small period of time and, therefore, scoring functions are often simplified to improve the speed and cost of the computational screenings [6]. These simplifications come with a cost in accuracy, which might not be problematic for one ligand-target situation but takes a much more challenging scope when talking about virtual screening of thousands or millions of compounds [5].The goal of virtual screening (VS) is to guide the selection of molecules for experimental testing. In these assays, millions of compounds are docked into one specific target and only a selection of the top scores proceeds for experimental testing. If a scoring functions fails to identify a potential strong binder, then, it remains hidden among those million compounds, despite their pharmacological potential. In fact, that is one of the main problems in VS, the false negatives, or molecules that the docki...

show abstract

Section: Introductionmentioning

confidence: 99%

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira

Sousa

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Binding free energy computed by MMGBSA or MMPBSA algorithms have been widely employed to predict the binding affinity of protein-ligand [ 38 , 39 , 40 , 41 ] and protein-protein complexes [ 16 , 42 , 43 , 44 ] and is in good agreement with experimental findings. In this work, binding free energy of Ank GAG 1D4 and Ank GAG 1D4-Y56A for HIV-1 CA was computed to understand the effect of single point mutation performed on residue Y56 of Ank GAG 1D4.…”

Section: Discussionmentioning

confidence: 76%

Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

et al. 2021

Self Cite

View full text Add to dashboard Cite

Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)—AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (−31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (−60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.

show abstract

“…Ebola causes hemorrhagic fever and molecular interactions between the monoclonal antibody ADI-15946 and the Ebola GPcl receptor is studied ( Hou and Zhang, 2020 ). Dengue is a tropical illness transmitted by mosquitoes, it is targeted with thioguanine small molecule inhibitors for the NS2B/NS3 protease ( Hariono et al, 2019 ) and antiviral peptides binding to the envelope protein domain III ( Isa et al, 2019 ). Therapies treating human papillomavirus targeting the E6 oncoprotein complex are also evaluated ( Ricci-Lopez et al, 2019 ).…”

Section: Applications To Drug Discoverymentioning

confidence: 99%

Recent Developments in Free Energy Calculations for Drug Discovery

King

Aitchison

et al. 2021

Front. Mol. Biosci.

View full text Add to dashboard Cite

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

show abstract

Dynamics and binding interactions of peptide inhibitors of dengue virus entry

Cited by 20 publications

References 54 publications

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Recent Developments in Free Energy Calculations for Drug Discovery

Contact Info

Product

Resources

About