Papers by Eko Aditya Rifai
Chemistry and Materials
Phyllanthin is known to have therapeutic properties such as immunomodulator, nephroprotective, an... more Phyllanthin is known to have therapeutic properties such as immunomodulator, nephroprotective, and anticancer. Phyllanthin is a lignan compound that is commonly found in plants of the Phyllanthus genus, one of which is green meniran (Phyllanthus niruri). Solvent Ionic Liquid (IL) is one of the alternative solvents that are widely used for the extraction of compounds from a plant. The purpose of this study were to compare the extraction yield of philanthine compounds from meniran herbs using IL solvent by Microwave Assisted Extraction (MAE) with methanol by maceration method, and to determine the IL solvent that could produce the highest phyllanthin content. Optimization were carried out using the independent variables IL solvent concentration (0.25 M, 0.75 M, and 1.25 M) and sample-solvent mixture (1:10, 1:12, and 1:14). The variable modeling is designed using Response Surface Methodology that produced 9 runs of combination of solvent concentration and sample-solvent mixture ratio. ...
Molecular dynamics study of autotaxin with potential allosteric inhibitors
Medicinal chemistry, Feb 6, 2017
Frontiers in Molecular Biosciences, 2020
Asian Journal of Pharmaceutical and Clinical Research, Oct 1, 2017
Objective: Malaria is a disease that impacts millions of people annually. Among the enzymes, plas... more Objective: Malaria is a disease that impacts millions of people annually. Among the enzymes, plasmepsin is the main enzyme in the plasmodium life cycle that degrades hemoglobin during the erythrocytic phase in the food vacuole. Recently, pharmaceutical industries have been trying to develop therapeutic agents that can cure malaria through the discovery of new plasmepsin inhibitor compounds. One of the developing approaches is the in silico method. Methods: The chosen in silico screening method in this experiment is a structure-based screening using GOLD software and the Indonesian medicinal plants database. Results: From ten in silico screening runs, three of the compounds always ranked in the top ten. These three compounds are trimyristin, cyanidin 3,5-di-(6-malonylglucoside), and isoscutellarein 4'-methyl ether 8-(6"-n-butylglucuronide). Another compound that emerged with high frequency is cyanidin 3,5-di-(6-malonylglucoside). Conclusions: Based on the results obtained from this screening, 11 inhibitor candidates are expected to be developed as antimalarial. These compounds are trimyristin; cyanidin 3,5-di-(6-malonylglucoside); isoscutellarein 4'-methyl ether 8-(6"-n-butylglucuronide); cyanidin 3-(6"-malonylglucoside)-5glucoside; multifloroside; delphinidin 3-(2-rhamnosyl-6-malonylglucoside); delphinidin 3-(6-malonylglucoside)-3',5'-di-(6-p-coumaroylglucoside); cyanidin 3-[6-(6-sinapylglucosyl)-2-xylosylgalactoside; kaempferol 3-glucosyl-(1-3)-rhamnosyl-(1-6)-galactoside; sanggenofuran A; and lycopene with a GOLD score range from 78.4647 to 98.2836. Two of them, Asp34 and Asp214, bind with all residues in the catalytic site of plasmepsin.
Journal of Chemical Information and Modeling, 2019
Binding free energy (ΔG bind) computation can play an important role in prioritizing compounds to... more Binding free energy (ΔG bind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔG bind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson−Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard singletrajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔG bind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔG bind calculations.
Journal of cheminformatics, Jan 21, 2017
Computational methods to predict binding affinities of small ligands toward relevant biological (... more Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein-ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy app...
Animal Reproduction Science, 2017
The aims of this study were to determinate whether pentoxifylline (PTX) increases the motion para... more The aims of this study were to determinate whether pentoxifylline (PTX) increases the motion parameters of fresh and frozen-thawed equine epididymal spermatozoa, to evaluate the tyrosine phosphorylation of frozen-thawed epididymal sperm in the presence of PTX and to determine whether the PTX-treatment of stallion epididymal sperm prior to freezing improves the fertility response of mares to a reduced number of spermatozoa per insemination dose. Fifty epididymis were flushed with a skim milk based extender with or without PTX. The pre-treatment with PTX enhanced the sperm motility after being harvested (P < 0.05); however the freeze-thaw process did not alter the sperm kinematics between control and treated samples (P > 0.05). Plasma membrane integrity did not differ between control and PTX group after recovery and after thawing (P > 0.05), as observed in tyrosine phosphorylation, which the PTX treatment did not alter the percentage of tail-associated immunofluorescence of cryopreserved epididymal sperm (P > 0.05). For the fertility trial, different insemination groups were tested: 800 × 10 6 epididymal sperm (C800); 100 × 10 6 epididymal sperm (C100); 100 × 10 6 epididymal sperm recovered in an extender containing PTX (PTX100). The conception rates for C800; C100 and PTX100 were 68.7% (11/16); 31.5% (5/16) and 50% (8/16), respectively. The conception rate did not differ among groups (P > 0.05), however, a low number of animals was used in this study. A trend toward significance (P = 0.07) was observed between C800 and C100 groups. In conclusion, PTX has no deleterious effect on sperm motility, viability and capacitation of cryopreserved stallion epididymal sperm. The conventional artificial insemination with 100 × 10 6 sperm recovered with PTX ensures acceptable conception rates and maximize the limited number of doses of cryopreserved stallion epididymal sperm.
Journal of Computer-Aided Molecular Design, 2017
Journal of Chemical Theory and Computation, 2020
Calculating free energies of binding (ΔG bind) between ligands and their target protein is of maj... more Calculating free energies of binding (ΔG bind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔG bind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔG bind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein−ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.
Scientific Reports, 2021
Numerous therapeutic compounds have been isolated from naturally abundant organic resources, whic... more Numerous therapeutic compounds have been isolated from naturally abundant organic resources, which may offer economical and sustainable sources of compounds with safe and efficacious biological activities. In the cosmetics industry, natural compounds with anti-aging activities are eagerly sought. Thus, we prepared various extracts from Rubus fraxinifolius leaves and used enzyme inhibition assays to isolate compounds with protective effects against skin aging. Two triterpenoids were isolated from Rubus fraxinifolius Poir. leaves. The structures were characterized by spectroscopic analyses (LC-ESI-MS, 1D/2D NMR) and comparison to reported data. Compound 1 and 2 were determined as 2,3-O-ethyleneglycol, 19-hydroxyurs-12-en-23,28-dioic acid and 2,3-O-propanediol,19-hydroxyurs-12-en-28-oic acid. Methanol extract and isolates were assessed for their inhibitory effects on elastase and tyrosinase. Compounds 1 and 2 inhibited elastase with IC50 122.199 µg/mL and 98.22 µg/mL, and also inhibite...
Frontiers in Molecular Biosciences, 2020
The linear interaction energy (LIE) approach is an end-point method to compute binding affinities... more The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy. This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of binding free energy for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on binding free energy computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and-dynamic based binding free energy calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).
Calculating free energies of binding (ΔGbind) between ligands and their target protein is of majo... more Calculating free energies of binding (ΔGbind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔGbind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔGbind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein−ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.
Binding free energy (ΔG bind) computation can play an important role in prioritizing compounds to... more Binding free energy (ΔG bind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔG bind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson−Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard single-trajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔG bind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔG bind calculations.
Objective: Malaria is a disease that impacts millions of people annually. Among the enzymes, plas... more Objective: Malaria is a disease that impacts millions of people annually. Among the enzymes, plasmepsin is the main enzyme in the plasmodium life cycle that degrades hemoglobin during the erythrocytic phase in the food vacuole. Recently, pharmaceutical industries have been trying to develop therapeutic agents that can cure malaria through the discovery of new plasmepsin inhibitor compounds. One of the developing approaches is the in silico method. Methods: The chosen in silico screening method in this experiment is a structure-based screening using GOLD software and the Indonesian medicinal plants database. Results: From ten in silico screening runs, three of the compounds always ranked in the top ten. These three compounds are trimyristin, cyanidin 3,5-di-(6-malonylglucoside), and isoscutellarein 4'-methyl ether 8-(6"-n-butylglucuronide). Another compound that emerged with high frequency is cyanidin 3,5-di-(6-malonylglucoside). Conclusions: Based on the results obtained from this screening, 11 inhibitor candidates are expected to be developed as antimalarial. These compounds are trimyristin; cyanidin 3,5-A; and lycopene with a GOLD score range from 78.4647 to 98.2836. Two of them, Asp34 and Asp214, bind with all residues in the catalytic site of plasmepsin.
Background: Computational methods to predict binding affinities of small ligands toward relevant ... more Background: Computational methods to predict binding affinities of small ligands toward relevant biological (off-) targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein-ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. Results: We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling. Conclusions: Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding.
Computational protein binding affinity prediction can play an important role in drug research but... more Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein–ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein–ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.
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Papers by Eko Aditya Rifai