In Silico Study of Membrane Lipid Composition Regulating Conformation and Hydration of Influenza Virus B M2 Channel

The Potential of Cyclodextrins as Inhibitors for the BM2 Protein: An In Silico Investigation

The influenza BM2 transmembrane domain (BM2TM), an acid-activated proton channel, is an attractive antiviral target due to its essential roles during influenza virus replication, whereas no effective inhibitors have been reported for BM2. In this study, we draw inspiration from the properties of cyclodextrins (CDs) and hypothesize that CDs of appropriate sizes may possess the potential to act as inhibitors of the BM2TM proton channel. To explore this possibility, molecular dynamics simulations were employed to assess their inhibitory capabilities. Our findings reveal that CD4, CD5, and CD6 are capable of binding to the BM2TM proton channel, resulting in disrupted water networks and reduced hydrogen bond occupancy between H19 and the solvent within the BM2TM channel necessary for proton conduction. Notably, CD4 completely obstructs the BM2TM water channel. Based on these observations, we propose that CD4, CD5, and CD6 individually contribute to diminishing the proton transfer efficiency of the BM2 protein, and CD4 demonstrates promising potential as an inhibitor for the BM2 proton channel.

Active Binding Modes of Caffeine with Cytochrome P450 1A2 Determine Its Metabolite Profiles

Caffeine is a very common kind of nervous stimulant, and it is primarily metabolized by Cytochrome P450 1A2 (CYP1A2) in the human body. Over the years, determining the interactions between caffeine and CYP1A2 has been a tough issue. The active binding modes and the catalytic regioselectivity of the metabolism between CYP1A2 and caffeine remain unclear. Here, to investigate the interactions between CYP1A2 and caffeine, we constructed the all-sequence CYP1A2-caffeine-membrane system using a multiple template approach. According to our simulation results, four active binding modes between CYP1A2 and caffeine that correspond to the four metabolic sites of caffeine are determined. What is more, a pre-reaction state for the CYP1A2-catalyzed reaction at caffeine's N3 site is identified. A more preponderant active binding mode might be the reason why the N3 site of caffeine becomes the primary metabolic site. Our findings could enhance our knowledge of the interactions between CYP1A2 and caffeine and help us better understand the regioselectivity of the metabolism between CYP1A2 and caffeine.

Molecular origins of asymmetric proton conduction in the influenza M2 channel

The M2 proton channel of influenza A is embedded into the viral envelope and allows acidification of the virion when the external pH is lowered. In contrast, no outward proton conductance is observed when the internal pH is lowered, although outward current is observed at positive voltage. Residues Trp41 and Asp44 are known to play a role in preventing pH-driven outward conductance, but the mechanism for this is unclear. We investigate this issue using classical molecular dynamics simulations with periodic proton hops. When all key His37 residues are neutral, inward proton movement is much more facile than outward movement if the His are allowed to shuttle the proton. The preference for inward movement increases further as the charge on the His37 increases. Analysis of the trajectories reveals three factors accounting for this asymmetry. First, in the outward direction, Asp44 traps the hydronium by strong electrostatic interactions. Secondly, Asp44 and Trp41 orient the hydronium with the protons pointing inward, hampering outward Grotthus hopping. As a result, the effective barrier is lower in the inward direction. Trp41 adds to the barrier by weakly H-bonding to potential H+ acceptors. Finally, for charged His, the H3O+ in the inner vestibule tends to get trapped at lipid-lined fenestrations of the cone-shaped channel. Simulations qualitatively reproduce the experimentally observed higher outward conductance of mutants. The ability of positive voltage, unlike proton gradient, to induce an outward current appears to arise from its ability to bias H3O+ and the waters around it toward more H-outward orientations.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Understanding the conformational changes in the influenza B M2 ion channel at various protonation states

The characterization of influenza (A/B M2) ion channels is very important as they are potential binding sites for the drugs. We report the all-atom molecular dynamics study of the influenza B M2 ion channel in the presence of explicit solvent and lipid bilayers using the high resolution solid-state NMR structures. The importance of the various protonation states of histidine in the activation of the ion channel is discussed. The conformational changes at the closed and the open structures clearly show that the increase in tilt angle is necessary for the activation of the ion channel. Additionally, the free energy surfaces of the eight systems show the importance of the protonation state of the histidine residues in the activation of the influenza B M2 ion channel. The protonation of the histidine residues increases the tilt angle and the intra-helix distance which is evident from the superimposition of the structures corresponding to the maxima and the minima in the free energy landscape. The findings imply differences in the singly protonated and double protonated conformational states of BM2 ion channel and provide insights to help further studies of these ion channels as the drug targets for the influenza virus.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Biological activities of drug inactive ingredients

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

In Silico Analysis Revealed a Unique Binding but Ineffective Mode of Amantadine to Influenza Virus B M2 Channel

The M2 proton channel of influenza A (AM2) and B (BM2) have a highly conserved function motif, considered as the effective target. As yet, there is no effective drug against BM2. Research showed that AM2 channel blocker, amantadine (AMT), was able to bind to BM2 channel, but AMT lacked inhibition against BM2. Nevertheless, the study of the binding but ineffective mode of AMT to BM2 is challenging. To resolve the challenge and obtain more information for drug design of inhibitors targeting BM2, multiple molecular dynamics simulations were performed. We discovered AMT mainly adopted up binding mode in BM2, involved in a transition flipping from down mode to up mode. Furthermore, we discovered a new key factor to explain ineffective inhibition of AMT to BM2 because of the unmatched spatial geometry between AMT and BM2. Our work could enrich structural feature information on BM2 and provide a new perspective for rational drug design of anti-influenza B.