Fengchao Cui

Investigate the Binding of Catechins to Trypsin Using Docking and Molecular Dynamics Simulation

To explore the inhibitory mechanism of catechins for digestive enzymes, we investigated the binding mode of catechins to a typical digestive enzyme-trypsin and analyzed the structure-activity relationship of catechins, using an integration of molecular docking, molecular dynamics simulation and binding free energy calculation. We found that catechins with different structures bound to a conservative pocket S1 of trypsin, which is comprised of residues 189–195, 214–220 and 225–228. In the trypsin-catechin complexes, Asp189 by forming strong hydrogen bonding, and Gln192, Trp215 and Gly216 through hydrophobic interactions, all significantly contribute to the binding of catechins. The number and the position of hydroxyl and aromatic groups, the structure of stereoisomers, and the orientation of catechins in the binding pocket S1 of trypsin all affect the binding affinity. The binding affinity is in the order of Epigallocatechin gallate (EGCG) > Epicatechin gallate (ECG) > Epicatechin (EC) > Epigallocatechin (EGC), and 2R-3R EGCG shows the strongest binding affinity out of other stereoisomers. Meanwhile, the synergic conformational changes of residues and catechins were also analyzed. These findings will be helpful in understanding the knowledge of interactions between catechins and trypsin and referable for the design of novel polyphenol based functional food and nutriceutical formulas.

Caffeoylquinic acids competitively inhibit pancreatic lipase through binding to the catalytic triad

Caffeoylquinic acid and its isomers inhibited porcine Pancreatic Lipase (PL) activity according to a competitive mode where binding and interaction with the catalytic triad of Ser153, His264 and Asp177 simultaneously occurred. The IC50 values under which 3-caffeoylquinic acid (CQA) and its isomers 4-, 5-CQA, 3,4-, 3,5- and 4,5-diCQA inhibited half of the porcine PL activity were 1.10, 1.23, 1.24, 0.252, 0.591 and 0.502 mM, respectively. The binding affinities in the range from -8.4 to -9.5 kCal/mol were well predicted from docking, which showed a high linear correlation coefficient of 0.893 and Spearman correlation of 1.0 with log(IC50) values. Caffeoylquinic acid and its isomers were stabilized by hydrogen bond and hydrophobic interaction in the binding pocket. This finding provided molecular mechanism of coffee and other natural food or drink containing caffeoylquinic acid and its isomers against lipase activity.

Small-Angle X-ray Scattering Study of Protein Complexes with Tea Polyphenols

Exploration of the structure of protein complexes, especially the change in conformation and aggregation behavior of proteins upon ligand binding, is crucial to clarify their bioactivities at the molecular level. We applied solution small-angle X-ray scattering (SAXS) to study the complex structure of bovine serum albumin (BSA) and trypsin binding with tea polyphenols, that is, catechin and epigallocatechin gallate (EGCG). We found that tea polyphenols can steadily promote the aggregation of proteins and protein complexes through their bridging effect. The numbers of proteins in the complexes and in the aggregates of complexes are extracted from SAXS intensity profiles, and their dependences as a function of the molar ratio of polyphenol to protein are discussed. EGCG has stronger capability than catechin to promote complex formation and further aggregation, and the aggregates of complexes have a denser core with a relatively smooth surface. The aggregates induced by catechin are loosely packed with a rough surface. BSA shows higher stability than trypsin in the formation of complex with a well-folded conformation. The synergistic unfolding of trypsin results in larger aggregates in the mixtures with more tea polyphenols. The binding affinity and number of tea polyphenols bound to each protein are further determined using fluorescence spectroscopy. The structure of protein complexes explored in this work is referable in the preparation of protein complex-based particles and the understanding of polyphenol-induced formation and further aggregation of protein complexes.

GPU-Accelerated Molecular Dynamics Simulation to Study Liquid Crystal Phase Transition Using Coarse-Grained Gay-Berne Anisotropic Potential.

Gay-Berne (GB) potential is regarded as an accurate model in the simulation of anisotropic particles, especially for liquid crystal (LC) mesogens. However, its computational complexity leads to an extremely time-consuming process for large systems. Here, we developed a GPU-accelerated molecular dynamics (MD) simulation with coarse-grained GB potential implemented in GALAMOST package to investigate the LC phase transitions for mesogens in small molecules, main-chain or side-chain polymers. For identical mesogens in three different molecules, on cooling from fully isotropic melts, the small molecules form a single-domain smectic-B phase, while the main-chain LC polymers prefer a single-domain nematic phase as a result of connective restraints in neighboring mesogens. The phase transition of side-chain LC polymers undergoes a two-step process: nucleation of nematic islands and formation of multi-domain nematic texture. The particular behavior originates in the fact that the rotational orientation of the mesogenes is hindered by the polymer backbones. Both the global distribution and the local orientation of mesogens are critical for the phase transition of anisotropic particles. Furthermore, compared with the MD simulation in LAMMPS, our GPU-accelerated code is about 4 times faster than the GPU version of LAMMPS and at least 200 times faster than the CPU version of LAMMPS. This study clearly shows that GPU-accelerated MD simulation with GB potential in GALAMOST can efficiently handle systems with anisotropic particles and interactions, and accurately explore phase differences originated from molecular structures.

EGFP-Based Protein Nanoparticles with Cell-Penetrating Peptide for Efficient siRNA Delivery

Development of an innovative nucleic acid nanocarriers still represents a challenge. In this study, we develop a protein nanoparticle (H6-TatEGFP) and examine its siRNA condensing activity. Gel retardation assay show that protein nanoparticle can condense siRNA into stable nanoparticle/siRNA complexes. UsingCy3-labelled siRNA, we also evaluate siRNA transport characteristic of protein nanoparticles in tumor cells, the results indicate that H6-TatEGFP nanoparticle may be a potential nanocarrier for siRNA in tumor cells.

Construction of explicit models to correlate the structure and the inhibitory activity of aldose reductase: Flavonoids and sulfonyl‐pyridazinones as inhibitors

The correlation between binding energies and bioactivities is the core of structure‐based computer‐aided drug design. However, many models to address this correlation are still strongly system‐dependent at current stage. We constructed two explicit models to correlate the binding energies with the inhibitory activities of flavonoids and sulfonyl‐pyridazinones as inhibitors of aldose reductase. The introduction of multiple complex states comprised of protein, coenzyme, substrate, and inhibitor can remarkably improve the correlation coefficients, compared with that using single complex state. Recombination of energy terms from complex structures and molecular descriptors of inhibitors can further improve the correlation. The explicit models provide correlation coefficients of 0.90 and 0.92 for flavonoids and sulfonyl‐pyridazinones, respectively. These models also steadily present the contribution from each energy term and the favorite of protein–inhibitor complex states. Meanwhile, we also observed that some inhibitors can accommodate alternative sites out of the conserved binding pocket at the presence/absence of coenzyme and substrate. It is responsible for the remarkable change in the binding energies and thus significantly influences the correlation between the structures and the inhibitory activities. Overall, this work presents a rational way to construct reliable explicit models through the combination of multiple physically accessible complex states, even though each of them only bears marginal information related to their activities.

Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations

Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation.

Assembled Structures of Perfluorosulfonic Acid Ionomers Investigated by Anisotropic Modeling and Simulations

Nafion, a classic of perfluorosulfonic acid ionomers, has broad applications in proton conduction, attributed from the unique structures. However, a satisfactory structure model from theoretical calculation and simulation that can match with the well-known experimental observations is still absent. We performed GPU-accelerated molecular dynamics simulations to investigate the assembled structures of Nafion at different water contents based on an anisotropic coarse-grained model equipped with Gay-Berne potential. Accurate parameters for the coarse-grained model are collected by matching energy profiles based on density functional theory calculations. The results show that the hydrophilic phase in Nafion assemblies undergoes a crossover from isolated spherical clusters to interconnected cluster/channel networks with the increase of water content. We found the crystalline domains in polymer matrix and they are suppressed at elevated water content. These microphase-separated structures achieve quantitative agreement with existing experimental observations, including morphologies from electron microscopy and intensity profiles from scattering experiments. This work suggests that accurate consideration of the anisotropy is a key to reveal the formation of unique assembled structures of perfluorosulfonic acid ionomers at different water contents.

Understanding the inhibitory mechanism of tea polyphenols against tyrosinase using fluorescence spectroscopy, cyclic voltammetry, oximetry, and molecular simulations

Inhibiting the activity of tyrosinase is a very effective and safe way to prevent enzymatic browning in food and to resist pests in agriculture. Tea polyphenols (TPs), regarded as safe and non-toxic food additives, have been reported due to their potential inhibitory capability against tyrosinase, but their ambiguous inhibitory mechanisms have severely limited their application. In the present work, fluorescence spectroscopy, cyclic voltammetry (CV), oximetry and molecular simulation approaches were employed to shed light on the underlying inhibitory mechanisms of TPs with different structures including (+)-catechin, (−)-epicatechin gallate (ECG) and (−)-epigallocatechin gallate (EGCG) against tyrosinase. Fluorescence spectra show that the three TPs are capable of binding tyrosinase with a molar proportion of 1 : 1. The analysis of CV curves and oxygen utilization suggests that these three TPs can be oxidized by tyrosinase, revealing that these three TPs are suicide inhibitors of tyrosinase. Furthermore, ECG and catechin make tyrosinase irreversibly inactivated due to their catechol group (ring B) being catalyzed by tyrosinase through a cresolase-like pathway, while EGCG inhibits the activity of tyrosinase by competing with or delaying the oxidation of substrate. Molecular simulations further confirm that ring B of ECG and catechin makes a significant contribution to tyrosinase inhibitory activities, and has a direct interaction with the coupled binuclear copper ions in the optimal orientation required by the cresolase-like pathway.

Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols

Quantitative Structure-Activity Relationship (QSAR) models of tyrosinase inhibitors were built using Random Forest (RF) algorithm and evaluated by the out-of-bag estimation (R2OOB) and 10-fold cross validation (Q2CV). We found that the performances of QSAR models were closely correlated with the systematic errors of inhibitory activities of tyrosinase inhibitors arising from the different measuring protocols. By defining ERRsys, outliers with larger errors can be efficiently identified and removed from heterogeneous activity data. A reasonable QSAR model (R2OOB of 0.74 and Q2CV of 0.80) was obtained by the exclusion of 13 outliers with larger systematic errors. It is a clear example of the challenge for QSAR model that can overwhelm heterogeneous data from different experimental protocols.