A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions

Kalinowsky, Lena; Wéber, Júlia; Balasupramaniam, Shantheya; Baumann, Knut; Proschak, Ewgenij

doi:10.1021/acsomega.7b01194

Cited by 18 publications

(13 citation statements)

References 50 publications

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“…1 C). This is also supported by evidences from Kalinowsky et al [ 30 ]. Furthermore, the scoring function was also tested against a group of related eicosonoides – 20-hydroxyecdysone, farnesoic acid, farnesol, dinoprostone, methyl farnesoate, lipoxin A4, methroprene, JHI, JHII, and JHIII (control) - which have been shown to demonstrate relatively varied binding affinity to MJHBP in vitro (with respect to the control, JHIII) in a study by Kim et al [ 18 ].…”

Section: Methodssupporting

confidence: 83%

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

Ononamadu

Abdalla

Ihegboro

et al. 2021

Biochemistry and Biophysics Reports

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Dengue has become a huge global health burden. It is currently recognized as the most rapidly spreading mosquito-borne viral disease. Yet, there are currently no licensed vaccines or specific therapeutics to manage the virus, thus, scaling up vector control approaches is important in controlling this viral spread. This study aimed to identify and study in silico , potential anti-mosquito compounds targeting Juvenile hormone (JH) mediated pathways via the Mosquito Juvenile Hormone Binding Protein (MJHBP). The study was implemented using series of computational methods. The query compounds included pyrethroids and those derived from ZINC and ANPDB databases using a simple pharmacophore model in Molecular Operating Environment (MOE). Molecular docking of selected compounds’ library was implemented in MOE. The resultant high-score compounds were further validated by molecular dynamics simulation via Maestro 12.3 module and the respective Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) binding energies computed. The study identified compounds-pyrethroids, natural and synthetic - with high docking energy scores (ranging from 10.91–12.34 kcal/mol). On further analysis of the high-ranking (in terms of docking scores) compounds using MD simulation, the compounds - Ekeberin D4, Maesanin, Silafluofen and ZINC16919139- revealed very low binding energies (−122.99, −72.91 -104.50 and,-74.94 kcal/mol respectively), fairly stable complex and interesting interaction with JH-binding site amino acid residues on MJHBP. Further studies can explore these compounds in vitro/in vivo in the search for more efficient mosquito vector control.

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Section: Methodssupporting

confidence: 83%

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

Ononamadu

Abdalla

Ihegboro

et al. 2021

Biochemistry and Biophysics Reports

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“…A variety of similar MMP datasets exist in the literature, but not all of them are immediately amenable to setting up RBFE calculations or limited by other considerations. Therefore, the decision was made to compose a new dataset as we did not want to be limited to e.g., cases where crystal structures are available for both protein−ligand complexes (as for instance described by Kalinowsky et al 6 ), since one protein structure is sufficient to set up an RBFE calculation and the small structural changes in our MMPs make a change in ligand binding mode unlikely. Similarly, the Baumgartner et al 7 dataset construction followed a different objective and required 10 MMPs per protein data bank (PDB) ligand.…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 from Tyrchan and Evertsson), but they usually maintain one-dimensional (1D) (molecular fingerprint) or two-dimensional (2D) (topology and substructure) representations of molecules. Only recently information on the three-dimensional molecular structure began to be incorporated into MMP analysis − and therefore, few established approaches exist to generate three-dimensional (3D) models of e.g., ligand–receptor complexes of MMPs.…”

Section: Introductionmentioning

confidence: 99%

Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen–Deshalogen Matched Molecular Pairs

Cappel¹,

Mozziconacci²,

Braun³

et al. 2021

J. Chem. Inf. Model.

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In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of <1 kcal/mol despite the semi-automated calculation scheme. We quantify the accuracy of the optimized potential for liquid simulations (OPLS) force field to predict the effect of halogen addition to compounds, a commonly employed chemical modification in the design of drug-like molecules.

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“…The HLA-peptide complex structures are optimized with Amber10:EHT force field and Generalized Born solvation model in MOE. (5) Calculating affinity scores of the optimized structures in order to identify the peptides with high affinity to the target HLA molecule Scoring functions of Contact Energy [6], Affinity dG, London dG, and GBVI/WSA dG [7] available in MOE are calculated. Contact Energy estimates the transfer free energy based on the area of the water-accessible surface.…”

Section: Methodsmentioning

confidence: 99%

HLABAP: HLA Class I-Binding Antigenic Peptide Predictor

Kataoka¹,

Amari²,

Ikegami³

et al. 2020

CBIJ

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HLA (Human Leucocyte Antigen) class I molecules present a variable but limited repertoire of antigenic peptides for T-cell recognition. Identification of specific antigenic peptides is essential for the development of immunotherapy. High polymorphism of HLA genes and a large number of possible peptides to be evaluated, however, have made the identification by experiments costly and time-consuming. Computational methods have been proposed to address this problem. In cases where plenty number of binding affinity data of peptides are available, various QSAR and machine learning approaches efficiently evaluate the affinity of test peptides, while in the cases where just a little data are available, structure-based approaches like elaborate docking have been proposed. We have developed a software named HLABAP that is designed to predict the binding affinities for a set of peptides against a particular HLA class I allele. By the combination of homology modeling for posing instead of docking and geometry optimization of the complex structures between the HLA molecule and peptides, HLABAP well predicts the binding affinities for the peptides. The results have shown that HLABAP should be applicable to identify possible antigenic peptides against a particular allele of HLA class I prior to the experiments far efficiently than the ordinary docking methods.

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A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions

Cited by 18 publications

References 50 publications

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

In silico identification and study of potential anti-mosquito juvenile hormone binding protein (MJHBP) compounds as candidates for dengue virus - Vector insecticides

Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen–Deshalogen Matched Molecular Pairs

HLABAP: HLA Class I-Binding Antigenic Peptide Predictor

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