Drug inhibition and proton conduction mechanisms of the influenza a M2 proton channel.

The influenza A virus matrix protein 2 (M2 protein) is a pH-regulated proton channel embedded in the viral membrane. Inhibition of the M2 proton channel has been used to treat influenza infections for decades due to the crucial role of this protein in viral infection and replication. However, the widely-used M2 inhibitors, amantadine and rimantadine, have gradually lost their efficiencies because of naturally-occurring drug resistant mutations. Therefore, investigation of the structure and function of the M2 proton channel will not only increase our understanding of this important biological system, but also lead to the design of novel and effective anti-influenza drugs. Despite the simplicity of the M2 molecular structure, the M2 channel is highly flexible and there have been controversies and arguments regarding the channel inhibition mechanism and the proton conduction mechanism. In this book chapter, we will first carefully review the experimental and computational studies of the two possible drug binding sites on the M2 protein and explain the mechanisms regarding how inhibitors prevent proton conduction. Then, we will summarize our recent molecular dynamics simulations of the drug-resistant mutant channels and propose mechanisms for drug resistance. Finally, we will discuss two existing proton conduction mechanisms and talk about the remaining questions regarding the proton-relay process through the channel. The studies reviewed here demonstrate how molecular modeling and simulations have complemented experimental work and helped us understand the M2 channel structure and function.

Structural and energetic analysis of drug inhibition of the influenza A M2 proton channel.

The type A influenza virus matrix protein 2 (M2) is a highly selective proton channel in the viral envelope. Because of its crucial role in viral infection and replication, the M2 channel has been a target of anti-influenza drugs. Due to the occurrence of drug-resistant mutations in the M2 channel, existing anti-influenza drugs that block the M2 channel, such as amantadine and rimantadine, have lost their efficacy against these mutant channels. Recent experimental and computational efforts have made great progress in understanding the drug resistance mechanisms of these mutations as well as designing novel drug candidates to block the mutant M2 channels. In this review, we briefly summarize the structural characteristics of the M2 channel, and then we discuss these recent studies on drug resistance and drug design of the mutant channels, focusing on the structures and energetics. We show that structural biology experiments and molecular modeling have led to the successful design of novel drugs targeting mutant M2 channels.

Structural comparison of the wild-type and drug-resistant mutants of the influenza A M2 proton channel by molecular dynamics simulations.

The influenza A M2 channel in the viral envelope is a pH-regulated proton channel that is crucial for viral infection and replication. Amantadine and rimantadine are two M2 inhibitors that have been widely used as anti-influenza drugs. However, due to naturally occurring drug-resistant mutations, their inhibition ability has gradually decreased. These drug-resistant mutations are found at various positions on the transmembrane domain of the M2 protein and could be categorized to three types: mutations close to the drug-binding site located at the pore-facing positions (V27A, A30T, S31N, and G34E); mutations at the interhelical interfaces at the N-terminal half of the channel (L26F); and mutations outside the drug-binding site lying at the interhelical interfaces (L38F, D44A). Investigating the structures and the M2-inhibitor interactions of these mutants would illuminate drug inhibition and drug resistance mechanisms and guide the design of novel anti-influenza drugs targeting these drug-resistant mutants. In this study, we chose four mutations at different positions (V27A, S31N, L26F, L38F) and conducted molecular dynamics simulations on both the apo-form and the drug-bound forms. The protein structures as well as the water structure in the channel pore were analyzed. Stable water clusters facilitating drug binding were found. Both the protein pore radius profiles and the structure of the water clusters were sensitive to the mutations. Based on our simulations, we compared the structures of the mutated proteins and proposed possible mechanisms for drug resistance of these mutations.

CYP-nsSNP: a specialized database focused on effect of non-synonymous SNPs on function of CYPs.

The cytochrome P450 (CYP) enzymes play the central role in synthesis of endogenous substances and metabolism of xenobiotics. The substitution of single amino acid caused by non-synonymous single nucleotide polymorphism (nsSNP) will lead to the change in enzymatic activity of CYP isozymes, especially the drugmetabolizing ability. CYP-nsSNP is a specialized database focused on the effect of nsSNPs on enzymatic activity of CYPs. Its unique feature lies in providing the qualitative and quantitative description of the CYP variants in terms of enzymatic activity. In addition, the database also offers the general information about nsSNP and compounds that are involved in corresponding enzymatic reaction. The current CYP-nsSNP can be accessible at http://cypdatabase.sjtu.edu.cn/ and includes more than 300 genetic variants of 12 CYP isozymes together with about 100 compounds. In order to keep the accuracy of information within database, all experimental data were collected from the scientific literatures, and the users who conducted research to identify the novel CYP variants are encouraged to contribute their data. Therefore, CYP-nsSNP can be considered as a valuable source for experimental and computational studies of impact of genetic polymorphism on the function of CYPs.

Atomistic modeling of protein-DNA interaction specificity: progress and applications.

An accurate, predictive understanding of protein-DNA binding specificity is crucial for the successful design and engineering of novel protein-DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein-DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL-DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein-DNA binding prediction and engineering is a goal within reach.

Classification Models for Predicting Cytochrome P450 Enzyme-Substrate Selectivity.

Cytochrome P450 (CYP) is an important drug-metabolizing enzyme family. Different CYPs often have different substrate preferences. In addition, one drug molecule may be preferentially metabolized by one or more CYP enzymes. Therefore, the classification and prediction of substrate specificity of CYP enzymes are of importance to the understanding of drug metabolisms and may help guide the development of new drugs. In this study, we used three different machine learning methods to classify CYP substrates for predicting CYP-substrate specificity based solely on structural and physicochemical properties of the substrates. We first built a simple decision tree model to classify substrates of four CYP enzymes, 1A2, 2C9, 2D6 and 3A4 with more than 78 % classification accuracy. We then built a single-label eight-class model and a multilabel five-class model to classify substrates of eight CYP enzymes and to classify substrates that can be metabolized by more than one CYP enzymes, respectively. Above 90 % and >80 % prediction accuracy was achieved for the single-label and multilabel models, respectively. The main improvement of our models over existing ones is the automated and unbiased selection of descriptors by genetic algorithms, which makes our methods applicable for larger data sets and increased number of CYP enzymes.

Free energy calculations on the two drug binding sites in the M2 proton channel.

Two alternative binding sites of adamantane-type drugs in the influenza A M2 channel have been suggested, one with the drug binding inside the channel pore and the other with four drug molecule S-binding to the C-terminal surface of the transmembrane domain. Recent computational and experimental studies have suggested that the pore binding site is more energetically favorable but the external surface binding site may also exist. Nonetheless, which drug binding site leads to channel inhibition in vivo and how drug-resistant mutations affect these sites are not completely understood. We applied molecular dynamics simulations and potential of mean force calculations to examine the structures and the free energies associated with these putative drug binding sites in an M2-lipid bilayer system. We found that, at biological pH (~7.4), the pore binding site is more thermodynamically favorable than the surface binding site by ~7 kcal/mol and, hence, would lead to more stable drug binding and channel inhibition. This result is in excellent agreement with several recent studies. More importantly, a novel finding of ours is that binding to the channel pore requires overcoming a much higher energy barrier of ~10 kcal/mol than binding to the C-terminal channel surface, indicating that the latter site is more kinetically favorable. Our study is the first computational work that provides both kinetic and thermodynamic energy information on these drug binding sites. Our results provide a theoretical framework to interpret and reconcile existing and often conflicting results regarding these two binding sites, thus helping to expand our understanding of M2-drug binding, and may help guide the design and screening of novel drugs to combat the virus.

Long-range effects of a peripheral mutation on the enzymatic activity of cytochrome P450 1A2.

The human cytochrome P450 1A2 is an important drug metabolizing and procarcinogen activating enzyme. An experimental study found that a peripheral mutation, F186L, at ∼26 Šaway from the enzyme's active site, caused a significant reduction in the enzymatic activity of 1A2 deethylation reactions. In this paper, we explored the effects of this mutation by carrying out molecular dynamics simulations and structural analyses. We found that the long-range effects of the F186L mutation were through a change in protein flexibility and a collective protein motion that caused the main substrate access channel to be mostly closed in the mutant. Our work is the first that combined both access channel analysis and protein motion analysis to elucidate mechanisms of mutation-induced allostery in a CYP protein. Such structural modeling and analysis approaches may be applied to other CYP proteins and other enzymes with buried active sites and may help guide protein engineering and drug design.

In silico prediction of cytochrome P450-mediated drug metabolism.

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.

Tethered-hopping model for protein-DNA binding and unbinding based on Sox2-Oct1-Hoxb1 ternary complex simulations.

The sliding and hopping models encapsulate the essential protein-DNA binding process for binary complex formation and dissociation. However, the effects of a cofactor protein on the protein-DNA binding process that leads to the formation of a ternary complex remain largely unknown. Here we investigate the effect of the cofactor Sox2 on the binding and unbinding of Oct1 with the Hoxb1 control element. We simulate the association of Oct1 with Sox2-Hoxb1 using molecular dynamics simulations, and the dissociation of Oct1 from Sox2-Hoxb1 using steered molecular dynamics simulations, in analogy to a hopping event of Oct1. We compare the kinetic and thermodynamic properties of three model complexes (the wild-type and two mutants) in which the Oct1-DNA base-specific interactions or the Sox2-Oct1 protein-protein interactions are largely abolished. We find that Oct1-DNA base-specific interactions contribute significantly to the total interaction energy of the ternary complex, and that nonspecific Oct1-DNA interactions are sufficient for driving the formation of the protein-DNA interface. The Sox2-Oct1 protein-protein binding interface is largely hydrophobic, with remarkable shape complementarity. This interface promotes the formation of the ternary complex and slows the dissociation of Oct1 from its DNA-binding site. We propose a simple two-step reaction model of protein-DNA binding, called the tethered-hopping model, that explains the importance of the cofactor Sox2 and may apply to similar ternary protein-DNA complexes.