Computational study of the binding mode of epidermal growth factor receptor kinase inhibitors

EXPLORING THE MOLECULAR BASIS OF H5N1 HEMAGGLUTININ BINDING WITH CATECHINS IN GREEN TEA: A FLEXIBLE DOCKING AND MOLECULAR DYNAMICS STUDY

The influenza A (H5N1) virus attracts a worldwide attention and calls for the urgent development of novel antiviral drugs. In this study, explicitly solvated flexible docking and molecular dynamics (MD) simulations were used to study the interactions between the H5N1 sub-type hemagglutinin (HA) and various catechin compounds, including EC ([–]-epicatechin), EGC ([–]-epigallocatechin), ECG ([–]-epicatechin gallate) and EGCG ([–]-epigallocatechin gallate). The four compounds have respective binding specificities and their interaction energies with HA decrease in the order of EGCG (-133.52) > ECG (-111.11) > EGC (-97.94) > EC (-83.39). Units in kcal mol-1. Residues IleA267, LysA269, ArgB68 and GluB78 play important roles during all the binding processes. EGCG has the best bioactivity and shows potential as a lead compound. Besides, the importance was clarified for the functional groups it was revealed that the C5′ hydroxyl and trihydroxybenzoic acid groups are crucial for the catechin inhibitory activities, especially the latter. Combined with the structural and property analyses, this work also proposed the way to effectively modify the functional groups of EGCG. The experimental efforts are expected in order to actualize the catechin derivatives as novel anti-influenza agents in the near future.

A water-based mechanism of specificity and resistance for lapatinib with ErbB family kinases

The dual kinase inhibitor lapatinib has a high affinity for EGFR and HER2 but a weak affinity for ErbB4, although the factors driving specificity for these highly homologous members of the ErbB family of receptor tyrosine kinases are not well understood. In this report, homology modeling, molecular dynamics simulations, and free energy calculations are employed with the goal of uncovering the energetic and structural molecular basis of lapatinib specificity and resistance. The results reveal a distinct network of three binding site water molecules that yield strikingly similar hydration patterns for EGFR and HER2 in contrast to that of ErbB4, which shows a different pattern with a reduced occupancy at one of the positions. The primary cause was traced to a single amino acid change in the binding site (EGFR position 775), involving a swap from C or S (EGFR and HER2) to V (ErbB4), for which the side chain is bulkier, is hydrophobic, and lacks the ability to form a H-bond with water. Notably, excellent quantitative agreement with experimental activities is obtained across the series (EGFR > HER2 > ErbB4) when key waters are included in the calculations. Quantitatively, Coulombic interactions and H-bond counts between network waters and species involved in the network are less favorable in ErbB4 by ~40% relative to those in EGFR or HER2. Additional simulations with clinically relevant EGFR (C775F, T854A, and T790M) and HER2 (T790I) mutants demonstrate that resistance can also be understood in terms of changes that occur in the binding site water network. Overall, the results of this study have yielded a physically reasonable water-based mechanism for describing lapatinib specificity and resistance.

Revealing interaction mode between HIV-1 protease and mannitol analog inhibitor

HIV protease is a key enzyme to play a key role in the HIV-1 replication cycle and control the maturation from HIV viruses to an infectious virion. HIV-1 protease has become an important target for anti-HIV-1 drug development. Here, we used molecular dynamics simulation to study the binding mode between mannitol derivatives and HIV-1 protease. The results suggest that the most active compound (M35) has more stable hydrogen bonds and stable native contacts than the less active one (M17). These mannitol derivatives might have similar interaction mode with HIV-1 protease. Then, 3D-QSAR was used to construct quantitative structure-activity models. The cross-validated q(2) values are found as 0.728 and 0.611 for CoMFA and CoMSIA, respectively. And the non-cross-validated r(2) values are 0.973 and 0.950. Nine test set compounds validate the model. The results show that this model possesses better prediction ability than the previous work. This model can be used to design new chemical entities and make quantitative prediction of the bioactivities for HIV-1 protease inhibitors before resorting to in vitro and in vivo experiment.

Computational study of CCR5 antagonist with support vector machines and three dimensional quantitative structure activity relationship methods

CCR5 is the key receptor of HIV-1 virus entry into host cells and it becomes an attractive target for antiretroviral drug design. To date, six types of CCR5 antagonist were synthesized and evaluated. To search more potent bio-active compounds, non-linear support vector machine was used to construct the relationship models for 103 oximino-piperidino-piperidine CCR5 antagonists. Then, comparative molecular field analysis and comparative molecular similarity indices analysis models were constructed after alignment with their common substructure. Twenty-one structural diverse compounds, which were not included in the support vector machine, comparative molecular field analysis, and comparative molecular similarity indices analysis models, validated these models. The results show that these models possess good predictive ability. When comparing between support vector machine and 3D-quantitative structure activity relationship models, the results obtained from these two methods are compatible. However, 3D-quantitative structure activity relationship model is significantly better than support vector machine model and previous reported pharmacophore model. These models can help us to make quantitative prediction of their bio-activities before in vitro and in vivo stages.

EGFR(S) inhibitors in the treatment of gastro-intestinal cancers: what's new?

In the past 10 to 15 years, a considerable progress has been made in the treatment of gastrointestinal (GI) related malignancies, as number of agents expanded from only one in 1995 to seven in 2006. Current review describes the recent role of targeted therapies, specifically EGFR inhibitors in the treatment of GI cancers. Importance of dietary agents in the treatment and prevention of GI cancers is also reviewed.

In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression

Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.

Molecular simulation of protein-surface interactions: benefits, problems, solutions, and future directions

While the importance of protein adsorption to materials surfaces is widely recognized, little is understood at this time regarding how to design surfaces to control protein adsorption behavior. All-atom empirical force field molecular simulation methods have enormous potential to address this problem by providing an approach to directly investigate the adsorption behavior of peptides and proteins at the atomic level. As with any type of technology, however, these methods must be appropriately developed and applied if they are to provide realistic and useful results. Three issues that are particularly important for the accurate simulation of protein adsorption behavior are the selection of a valid force field to represent the atomic-level interactions involved, the accurate representation of solvation effects, and system sampling. In this article, each of these areas is addressed and future directions for continued development are presented.