Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3
© Glišić et al. 2016
Received: 25 September 2015
Accepted: 14 March 2016
Published: 22 March 2016
The pathophysiological overlapping between Sjorgen’s Syndrome (SS) and HCV, presence of anti- muscarinic receptor type 3 (M3R) antibodies in SS, the role that M3R plays in the regulation of the heart rate, has led to the assumption that cardiovagal dysfunction in HCV patients is caused by anti-M3R antibodies elicited by HCV proteins or by their direct interaction with M3R.
To identify HCV protein which possibly is crossreactive with M3R or which binds to this receptor, we performed the Informational Spectrum Method (ISM) analysis of the HCV proteome. This analysis revealed that NS5A protein represents the most probable interactor of M3R or that this viral protein could elicit antibodies which modulate function of this receptor. Further detailed structure/function analysis of NS5A and M3R performed by the ISM method extended with other Digital Signal processing (DSP) approaches revealed domains of these proteins which participate in their crossreactivity or in their direct interaction, representing promising diagnostic and therapeutic targets.
Application of the ISM with other compatible bioinformatics methods offers new perspectives for identifying diagnostic and therapeutic targets for complicated forms of HCV and other viral infections. We show how the electron-ion interaction potential (EIIP) amino-acid scale used in the ISM combined with a robust, high performance hydrophobicity scale can provide new insights for understanding protein structure/function and protein-protein interactions.
The problem of understanding protein structure, function and binding epitopes from the sequence remains challenging. In this paper, we extended the ISM, [1, 2] that combines an Electron Ion Interaction Potential (EIIP) amino acid scale, with a high performance hydrophobicity scale along with a novel DFT algorithm . We show that ISM extended with other Digital Signal processing (DSP) approaches provides a unique way to understand the formation of protein domains and relation of primary sequence to the formation of secondary structure and the location of epitopes. We have used this method to understand virus-induced autoimmune diseases.
Lymphoproliferative disorders usually accompany persistent hepatitis C virus (HCV) infection . This leads to a number of extra-hepatic manifestations  one of them being the Sjögren’s syndrome (SS). This syndrome, characterized by the kerato-conjuctivitis sicca and xerostomia, was proven to be associated with the presence of autoantibodies for the muscarinic receptor type 3 (M3R). The association was confirmed both in the primary and secondary forms of this syndrome [6–8].
Anti-muscarinic antibodies have been found to be crucial for exocrine gland dysfunction , but also for other accompanying autonomic dysfunctions (sudomotor, cardio-vagal and adrenergic functions) . This important pathophysiological overlap between SS and HCV is further supported by data on cardio-vagal dysfunction in HCV [10–12]. Cardio-vagal dysfunction [13–18] is identified as one of the main reasons for increased mortality in chronic liver disease .
The mechanism of cardio-vagal lesion in HCV is still unknown. Some hypotheses have been proposed, like an immune mediated, common pathophysiological mechanism of hepatocellular damage and cardiac autonomic dysfunction [11, 12].
The acetylcholine muscarinic type 3 receptor (M3R), a G protein coupled receptor (GPCR), participates in the regulation of the heart rate and cardiac repolarization in animals  and humans [18, 21, 22]. Molecular signaling pathways participating in cardiac parasympathetic regulation, which include M3R, are still elusive, although some possible mechanisms have been proposed .
In addition to the heart, M3Rs are also present on progenitor hepatic cells . This pool of cells is of particular importance for the regenerative process in diseased livers, including chronic HCV infection. Vagal stimulation, mediated through M3R, is therefore of crucial importance by increasing the pool of hepatocyte progenitor cells .
Since the M3R participates both in the regulation of the heart rate [20, 22], and hepatocyte turnover [23, 24], we hypothesize that the part of the possible common mechanism of the lesion of cardiac parasympathetic regulation and hepatocyte compartment [11, 12] is through immune-modulatory autoantibodies to M3R.
A number of studies have investigated the potential role of molecular mimicry in autoimmune disorders associated with HCV infection [25–27]. It is interesting to note that immunological cross-reactivity between proteins has been shown to correspond to a common frequency component in their informational spectra (IS) [28–31]. Our objective in this work was therefore to look for a common frequency in the IS (Information Spectrum) of M3R and the HCV proteins allowing us to provide strong evidence that the cross-reactivity is indeed due to molecular mimicry. We performed an ISM analysis of all proteins from the EUHCV database. Our analysis showed that the NS5A protein, particularly from HCV viruses genotype 1b, represents the most probable antigen which is cross-reactive with M3R. We also mapped a domain and binding epitope of NS5A representing a potential prognostic and therapeutic target for the cardiac dysfunction caused by the HCV virus.
The dataset was created by extracting only the complete—full length HCV protein sequences from the GenBank polyprotein sequences. Human muscarinic acetylcholine receptor M3 was retrieved from UniProtKB/Swiss-Prot with accession number P20309. Prototype HCV protein sequences were retrieved from the EUHCV database .
Informational spectrum method (ISM)
The ISM starts by assigning a numerical value to each amino acid in the sequence that best represents the physico-chemical property of the amino acid involved in the structure and biological activity of the protein. In this paper, we have used two different, but related methods to assign numerical values to each of the amino acids in the primary sequence—one is the electron-ion interaction potential (EIIP) [33–38], and the other is from a hydrophobic proclivity scale . The next step in the ISM is to apply a Discrete Fourier Transform (DFT) and transform the protein primary (“time signal”) sequence into the frequency domain. In analyzing protein sequences, the relevant information is presented as an energy density spectrum (review in Reference 39) from the square of the Fourier Transform derived frequency amplitude coefficients obtained from the numerically encoded amino-acid sequence. The information defined by the sequence of amino acids is then available as an Information Spectrum (IS) representing a series of frequencies and their amplitudes. The total number of frequencies represented by the DFT amplitude/phase coefficients is limited by the Nyquist sampling theorem from information theory and the mathematics of the DFT algorithm. Each amino acid in the primary sequence is “sampled” at some “equal” frequency corresponding to the “time” intervals of one inter-amino acid step in the primary sequence. Due to the time and frequency symmetry, the reciprocal of each frequency f(n) from the DFT corresponds to a correlation of some step distance d(n) = 1/f(n) between amino acids in units of one amino-acid step distance. Per the Nyquist theorem, the maximum frequency (f(n = N)) will always be 0.5 corresponding to resolving a minimum inter-amino acid correlation distance of 2.0 amino-acids.
The IS frequencies f(n) corresponds to the distribution of structural motifs with defined physico-chemical characteristics responsible for the biological function of a protein and its binding affinities. Similarly, the amino-acid correlation distances d(n) provide information about the secondary and tertiary structure of the sampled protein, and about binding epitopes within proteins associated with long range inter-molecular attraction and binding. When comparing proteins which share the same biological or biochemical function, the ISM technique allows detection of protein-protein correlation pairs, whose common frequency amplitudes f(n) are specific for their common biological properties, or which correlate with their specific interaction. These common, dominant informational characteristic frequencies of the protein sequences are determined/pin-pointed by a Cross-spectrum or Consensus Informational Spectrum (CIS) (i.e. the Fourier transform of the auto-correlation function for the spectrum). In this way, any spectral component (frequency) not prominent in all compared IS spectrum’s are diminished and thereby eliminated. If one calculates a CIS (by multiplication of all the power spectrum values together at each corresponding frequency for each protein power spectrum) for a group of proteins having different primary structures, and finds strictly defined peak frequencies, it means that the analyzed proteins participate in mutual interactions or have a common biological functions.
We show in this paper how the ISM algorithm combines the method’s native EIIP scale with a robust hydrophobicity scale to provide a unique understanding into protein domains, functions and binding epitopes.
The hydrophobicity scale
Hydrophobicity is a primary physico-chemical property of amino-acids associated with the structure and function of proteins. Hydrophobicity is a real and measurable force between aqueous clathrate membranes that spontaneously form about hydrophobic surfaces; a force which can operate up to 100-200nm [39–41]. Since there is a range of amino-acid physico-chemical properties with respect to each other and with respect to water, a hydrophobicity scale must reflect continuum differences ranging between extremes of molecular size, mass, geometry, polarity/hydrophilicity and molecular non-polarity/hydrophobicity. Amino-acid hydrophobicity scales (with various drawbacks/defects) have proliferated in the literature, but effectively they measure the contrast of the strength of interaction between amino-acids and water as a means of devising an amino-acid scale/index. The hydrophobicity scale used in this study has been developed to overcome current literature amino-acid hydrophobicity scale limitations in order to more accurately reflect the central, net resultant of amino-acid physico-chemical properties which result in amino-acid hydrophobicity’s (e.g. their interaction potentials with water) .
The EIIP scale
The EIIP scale reflects the average energy of atomic valence shells and the resulting molecular orbitals of each amino-acid, acting as discrete electronic units, building up the electronic properties of peptides and proteins. The EIIP scale therefore reflects molecular properties from delocalized electron density. It is this latter property that allows proteins to have motifs with distinct wavelengths that support functions essential to specific protein functions and long range protein attractions [1, 2].
The joint dependencies (i.e. in phase relationships) of amino-acid EIIP and hydrophobicity properties reflect important functional epitopes or structural motifs within proteins, as we show in this current study. We postulate that the EIIP scale reflects the proclivity of a protein to form a QM/EM dipole capable of long range (up to 200 nm) attractions between proteins or proteins-substrates. Furthermore, we hypothesize that the inter-molecular QM/EM dipole interaction is transduced by aqueous clathrate shells (reflected by the hydrophobicity scale) at appropriate locations and transmitted through the bulk waters as a polaron mediated by aqueous phonons that operate in the low Terahertz frequency region.
The ISM algorithmic steps for generating an informational spectrum analysis
Convert a protein sequence into its numerical sequences with EIIP and hydrophobicity index values
Apply the discrete Fourier transformation to the resulting two numerical series to generate the power density spectrum.
Apply the Cross Informational Spectrum algorithm (i.e. multiply the corresponding frequency amplitudes squared) to the set of DFT spectral energies and obtain the CIS consensus spectrum to recover the dominant relational/inter-correlation frequency/frequencies.
Perform the sliding window DFT algorithm calculating signal to noise (S/N) calculations for each window location for each of the dominant CIS frequencies to identify the hot spot location(s) for each protein corresponding to binding epitopes/active sites being indicated by the ISM method.
For the hybrid algorithm introduced in this paper, for each protein run the Cross-Correlatoin Dependancy (CCD) analysis on the product of the EIIP encoded sequence and the hydrophobicity encoded sequence in order to recover the primary binding epitopes and confirm the conventional ISM hot spot locations.
Core equations describing the discrete fourier transform (DFT)
Cross-codependency (EIIP, hydrophobicity) DFT analysis
The Hydrophobicity proclivity values used in hybrid algorithm to encode the amino acids
A Cross-CoDependancy (CCD) analysis of amino-acid electronic and hydrophobicity in proteins detects whether or not the primary sequence of a given protein will have dominant and characteristic ISM frequencies for EIIP encoding and Hydrophobicity encoding that will have intersecting sequence “hot spots” that define important long range QM/EM dipole attraction sites and/or binding epitopes. If codependent hot spots exist in a given protein, then the periodic physico-chemical properties reflected by the EIIP and Hydrophobicity indices are basically in “phase.” The protein sequence hot spots are recovered by the standard positional ISM Signal to Noise ratio (S/N) analysis as illustrated in the results section of this paper .
Where Ww is the CCD-DFT window width that is calculated by selecting the largest of the EIIP or Hydrophobicity dominant correlation distances, as calculated from the reciprocal of the respective dominant ISM frequencies, which is rounded to the nearest integer. We show later, the Ww calculated for this ISM analysis of the HCV NS5A protein is 6. The ratio of Ww to the other correlation distance is expected to result in a ratio value near an integer (to which it will be rounded), if the phase relationship between the EIIP signal and the Hydrophobicity signals are close and one of the signals is close to a sub-harmonic of the other.
Microsoft Office 2010 Excel was used to calculate and plot the Fd(n = 0) and Fd(n = 1) series, where each Fd() is calculated for the sliding window of width Ww. The number of Fd() values is N - Ww. The Excel trend plot feature was used to plot the Fd() values. The Excel trend plot feature does a smooth curve fit of calculated values, which is related to a second order b-spline smoothing method. The Fd (n = 1) points result in a sinusoid of wavelength of 6 amino-acid steps. The Fd(n = 0) represents a special case where cos(Ɵ) = 1 and Fd(n = 0) is simply the sum of the Dc = EIIP*Hydrophobicity values for each sliding window of size Ww. The Fd(n = 0) coefficients represent the outer amplitude envelope of all sinusoid frequencies within each sliding window. The Fd(n = 1) plot sinusoid peaks shows the location of each putative intersection (phase crossing) of the EIIP and Hydrophobicity metrics (amino-acid sequence physico-chemical properties), where we also see the Fd(n = 1) positive peaks touch the Fd(n = 0) amplitude envelope. Additional confirmatory work was conducted using standard FFT methods on the NS5A domain I amino acids for both power (amplitude squared) and phase coefficients squared .
Docking simulations were done on a HEX Server . For protein-protein docking, using results from the ISM, interface residues were set from #513 to 551 in Muscarine M3 receptor (structure PDBID 4DAJ) and from #171 to 187 in HCV NS5A protein domain (structure PDBID 3FQQ). The range angles, which determine conformational space of both proteins, with origins in centroids of residue selections, were set to 45°. The number of output solutions was set to 100. The solution with best docking score was selected for further analysis.
Calculation of the cross-spectrum (CS) between M3R and each protein from the EUHCV database revealed that maximal amplitudes and signal-to-noise ratio (S/N) corresponds to the CS relationship between the M3R and HCV NS5A proteins, and that f(0.158) represents the common frequency component in IS for these two proteins using the EIIP scale.
The supplementary FFT analysis (length of 256) of the encoded domain I amino acids using the EIIP, hydrophobicity and joint (multiplied) scales generally confirms these primary functional wavelengths. Interestingly, the squared phase coefficient spectrum of the joint scale had two primary wavelengths of 2.15 as opposed to 2.08 and 6.24 as opposed to 6.33. These latter wavelength results are essentially the same as the native ISM method findings within the wavelength resolutions imposed by these basic DSP methods.
In particular, the pronounced valley’s in the blue Fd(0) trace dipping below 0.15 represent important regions of phase synchronization between the electronic (EIIP) configuration and the residue hydrophobicity’s, which reflect solvent like residue-residue contacts (senso-lato) and/or important residue contacts with the solvent (water, senso-stricto). The lowest hydrophobicity values represent the most hydrophobic residues, which then range up to higher values near one representing polar and then ionic residues. Hydrophobicity in the sense of the Hydrophobicity scale means that the hydrophobic residue contact with water promotes water-water surface film/membrane (clathrate) interactions. The Hydrophobicity scale also reflects information from the mass, volume, surface area, secondary group geometry and entropy of the 20 natural amino acids .
A list of the NS5A domain I epitopes indicated by the CCD-DFT analysis
Fd(0) valley (AA#) minima
Putative width of epitope effect range (#AA) about the epitope valley minima
(end of N terminal membrane anchor motif)
The NS5A domain #1 crystal structure (Fig. 7, PDBe 3fqq) shows the secondary structure is almost all beta sheets. The valleys in Fd(0) represent distinct and functionally active correlations between EIIP and the Hydrophobicity. Position #175 represents the epicenter of the domain #1 primary epitope, as this is the minimum Fd(0) amplitude valley. Position #198 on represents a radical shift in the amino-acid pattern signaling the end of domain #1. Positions 174–178 are a Beta strand (TFLV) and positions 180–183 are a Beta strand. These 2 strands are part of a 3 strand Beta sheet with a small intercalated alpha helix. The bound Zinc (Zn + 2) ion is located right at the alpha helix in the primary epitope area. The putative epitopes reported in Table 2 are consistent with reported literature epitopes within NS5A, and in particular the primary NS5A epitope has been reported as a NS5A dimer binding site (a 2 x 3 = 6 strand beta sheet), where the dimer is important for viral replication [45, 46]. In silico docking studies and site directed mutagenesis binding studies with a known NS5A activity inhibitor implicate sensitive AA sites: 21,24,28,30,31,38,54,56,58,62,75,92,93; where AA# 21&24 are in a membrane anchor alpha helix and the balance of these AA sites are within epitope ranges indicated in Table 2 .
The primary Domain I signal epitope is located in the center left of the Fig. 7, which is a motif consisting of a three strand Beta sheet and a small Alpha helix, and the primary signal epitope is located on two of the Beta strands in the indicated motif.
Hydrophobic contacts between muscarinic M3 receptor and NS5A, with corresponding amino-acid residues, highlighted atoms and distances and angles between them
Muscarinic M3 receptor (atom)
Bond angles/degrees (atoms)
ILE 70 (CB)
PHE 127 (CG-CD1-CD2-CE1-CE2-CZ centroid)
31.747 (ILE70 (CB)—PHE127 (centroid)—PHE 127 (CD1))
TYR 524 (CG-CD1-CD2-CE1-CE2-CZ centroid)
CYS 190 (SG)
21.30 (TYR 254 CZ-TYR 254 centroid—CYS 190(SG))
LEU 527 (CG)
PRO 189 (CB-CG-CD centroid)
133.20 (LEU 527(CG)—PRO 189(CA)—PRO 189(centroid)
ILE 534 (CG)
LEU 183 (CG)
48.88 ILE 534 (CG)—LEU 183 (CD2)—LEU 183(CG)
ILE 508 (CG)
LEU 179 (CG)
72.54 ILE 508 (CG)—LEU 179 (CD2)—LEU 179 (CG)
Finally, to determine domain which gives essential contribution to the information represented by the frequency F(0.158) in MR3, the ISM computer-assisted screening of the MR3 primary structure was performed. Identified domain denoted VINM3R is within the C-terminus of MR3, encompassing the third extracellular loop (ECL3) and the TM alpha helix #7 (residues 517–551). According to the ISM concept, this region would be immunologically crossreactive with NS5 or could be involved in the direct M3R/NS5A interaction.
DSP applications of the EIIP and hydrophobicity scales & experimental support
The ISM has been successfully applied in structure-function analysis of many different protein and DNA sequences, prediction of biological function of novel proteins, de novo design of biologically active peptides, assessment of biological effects of mutations, and identification of new therapeutic targets [36, 42].
For example a ISM DSP analysis using the EIIP scale could find known binding sites (hot spots) for 5 chemotherapeutic Tubulin binding agents and could also determine relative differences in binding affinities to these hot spots as applied to 10 human tubulin isotypes .
Similarly, a FFT power spectrum analysis of anti-microbial peptides found distinct and matching peaks in power spectra using hydrophobicity as the primary scale (Kyte & Doolittle hydropathy index) and confirmed with a composite scale with 4 other AA metrics .
An ISM like DFT method has been published  where hydrophobicity and EIIP were found to be especially robust AA physico-chemical scales (with 611 physico-chemical scales evaluated) for analyzing Influenza A strain proteins with identification of characteristic frequencies as found with the ISM DSP methods in this study. The ISM like DFT methods showed that the phase (sin()) coefficients squared spectrum was generally as informative for finding characteristic frequencies as was the power spectrum .
A DSP method has also been developed [1, 52] using a different algorithm than DFT derived methods, such as the ISM, using primary protein sequences encoded with a simple modified Nozaki–Tanford–Zimmerman hydrophobicity scale. This DSP method attempted to find “autocorrelation waves” or “hydrophobic eigenmodes,” using a lagged auto-covariance matrix decomposition and all poles power spectral and wavelet transformation. In practice this method finds characteristic structural/functional frequencies similar to the ISM method. This method was applied to Type I Tyrosine Kinase-coupled receptors and GPCR receptor proteins giving primary (often structural, such as in GPCR’s 7 transmembrane alpha helices) and secondary hydrophobicity activity wavelengths, that were then used to efficiently design (30 %–80 % hit rate) receptor agonist/antagonist binding peptides (aptamers, 8–20 residues) against these receptors (external loops), possessing matching inverted hydrophobicity structural modes. These results were confirmed in vivo, both at cellular and organismal levels (rat).
This same lagged eigen-function method was used to design aptamers against a globular protein (beta-galactosidase) and verified with temperature dependent kinetic studies with an ELISA method and the with Van't Hoff relation. The results indicated a classical linear free energy (enthalpy, entropy) relation characteristic of hydrophobic interactions of small and large bio-molecules in water. There was an entropy–enthalpy compensation relationship which showed signs of both competitive and non-competitive interactions .
Some of the references to studies using DSP methods to analyze protein primary structure using hydrophobicity scales and the EIIP scale illustrate the power of analyzing protein structure and function with these DSP methods, such as the ISM platform, are far more than mathematical curiosities’, but rather have experimentally supported real world applications.
Implications for clinical research
The pathophysiological overlapping between SS and HCV, presence of anti-M3R antibodies in SS, the role that M3R plays in the regulation of the heart rate and cardiac repolarization [18, 20–22], has led to the assumption that cardiovagal dysfunction in HCV patients is caused by anti-M3R antibodies elicited by HCV proteins, or by their direct interaction with M3R. In order to identify the HCV protein which possibly is cross-reactive with M3R, or which binds to this receptor, we performed an ISM analysis of the HCV proteome. Our analysis has revealed that the NS5A protein represents the possible interactor with the M3R receptor, or that this viral protein elicits antibodies which modulate function of this receptor. Comparison of the ISM of NS5A from different HCV genotypes showed that genotype 1 has the highest value of S/N on the frequency f(0.158) suggesting that these proteins are the most probable HCV pathological modulators of the biological activity of M3R (Fig. 1). Computer-assisted screening revealed that domains VINNS5A (residues 171–187) and VINM3R (residues 517–551) are probably involved in immunological cross-reactivity or in direct interaction between NS5A and M3R. The VINM3R peptide corresponds to part of the extracellular loop 3 and the TM alpha helix #7.
Informational similarity could result in immunological cross-reactivity which was experimentally shown in previous studies . In the current study we have identified AA 171–187 of the HCV NS5A as the domain contributing the most to the characteristic information important for cross-reactivity. Evidence from various studies carried out with NS5-derived antigens has demonstrated that they are not as immune-reactive as the other antigens [53, 54], and could be less specific . In contrast to earlier findings, Rodríguez-López et al. , we have found the NS5A domain I region to be among the most immunogenic. In accordance with the present results, in their experiments studying the immunogenicity of the variable regions of HCV proteins by ELISA using synthetic peptides from 120 variable regions in sera from HCV-infected cells, they have found the primary epitope, mapped to region 2144–2149 (172–177 NS5A) inside the ISM identified NS5A domain I with the motif (2144) DVTFQV (2149). This finding provides further support for our hypothesis of NS5A as possible immunogenic epitope.
Another important data is that ISM identified domain VINM3R in M3R is overlapping with functional epitopes of M3R reported by Koo et al.  (residues 517–527) and Tsuboi et al.  (residues 517–530) within the M3 third extracellular loop which interacts with autoantibodies from SS patients. This finding suggest that it cannot be excluded that this antibodies could be also elicited against NS5A in SS patients infected with HCV, supporting the finding of an involvement of HCV in the development of SS in a specific subset of patients .
Data from the literature support a link between antibodies against M3R and functional inhibition of the M3R receptor. The agonist binding site occupation by antibodies against M3R was reported as the key mechanism for loss of M3R function  and new evidence on the role of antibodies against M3R in M3R internalization has been presented . Also it should not be ignored that the TM alpha helix #7 has been reported  as an important allosteric binding site and consequentially a chance exists that an impaired function of M3R could be mediated through allosteric inhibition by antibodies against M3R.
Another possibility according to the ISM results is direct interaction between NS5A and M3R. Based on ISM and protein-protein docking results, we propose that, in conditions of a water medium, there is possibility that those two proteins interact and form a complex, like that of an antigen-antibody. The stabilization is based on hydrophobic interactions of targeted regions, which are favored in the water medium. NS5A can act as potential inhibitor of M3R, or could result in impaired trafficking of the M3R to the cell surface. In order to test all these hypothesis and potential molecular mechanisms of NS5A and M3 interaction, experimental studies can be designed with guidance from our DSP analysis. These experimental data should put novel light on the subject of progenitor hepatic cell apoptosis and the phenomena of HCV overlap with Sjöngren syndrome , or parasympathetic lesion .
Finally, the N-terminal of M3R region, which was identified as the region contributing the most to the characteristic f(0.158) frequency, could be the region of special interest for the pharmacological design of the substances that could block cross-reaction with M3R [30, 31], and in this way preventing cardiac dysautonomy and M3R dependent hepatocellular damage in HCV patients. It is also plausible that the mutations in this region or in other regions that cause the conformational changes of M3R could induce protective or susceptibility effects for different extra-hepatic manifestations of HCV caused by the lesion of M3R.
In conclusion, our present study could have important clinical implications, such as the possibility of designing of aptamer peptides to sequester viral proteins in order to preclude cellular receptor binding and resulting in aptamer peptide-protein complexes that could be cleared by the immune system. Presented advances in the ISM platform could empower DSP methods to become mainstream tools for analyzing protein structure/function and to bring about a foundation for better therapeutics.
This work was supported by Ministry of Education, Science and Technological Development of the Republic of Serbia, Grants number III 41028 and OI 173001. We acknowledge help from the University of Alabama in Huntsville.
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- Selz KA, Samoylova TI, Samoylov AM, Vodyanoy VJ, Mandell AJ. Designing allosteric peptide ligands targeting a globular protein. Biopolymers. 2007;85(1):38–59. doi:10.1002/bip.20607.View ArticlePubMedGoogle Scholar
- Chrysostomou C, Seker H, Aydin N. CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences. Adv Bioinformatics.2015:909765. doi:10.1155/2015/909765
- Cavanaugh DP, Chittur KK. A hydrophobic proclivity index for protein alignments. F1000Research. 2015;in press.Google Scholar
- Zignego AL, Ferri C, Pileri SA, Caini P, Bianchi FB. Extrahepatic manifestations of Hepatitis C Virus infection: a general overview and guidelines for a clinical approach. Dig Liver Dis. 2007;39(1):2–17. doi:10.1016/j.dld.2006.06.008.View ArticlePubMedGoogle Scholar
- Agnello V, De Rosa FG. Extrahepatic disease manifestations of HCV infection: some current issues. J Hepatol. 2004;40(2):341–52.View ArticlePubMedGoogle Scholar
- Waterman SA, Gordon TP, Rischmueller M. Inhibitory effects of muscarinic receptor autoantibodies on parasympathetic neurotransmission in Sjogren’s syndrome. Arthritis Rheum. 2000;43(7):1647–54. doi:10.1002/1529-0131(200007)43:7<1647::AID-ANR31>3.0.CO;2-P.View ArticlePubMedGoogle Scholar
- Park K, Haberberger RV, Gordon TP, Jackson MW. Antibodies interfering with the type 3 muscarinic receptor pathway inhibit gastrointestinal motility and cholinergic neurotransmission in Sjogren’s syndrome. Arthritis Rheum. 2011;63(5):1426–34. doi:10.1002/art.30282.View ArticlePubMedGoogle Scholar
- Sumida T, Tsuboi H, Iizuka M, Asashima H, Matsumoto I. Anti-M3 muscarinic acetylcholine receptor antibodies in patients with Sjogren’s syndrome. Mod Rheumatol. 2012;23(5):841–5. doi:10.1007/s10165-012-0788-5.View ArticlePubMedGoogle Scholar
- Wright RA, Grant IA, Low PA. Autonomic neuropathy associated with sicca complex. J Auton Nerv Syst. 1999;75(1):70–6.View ArticlePubMedGoogle Scholar
- Ammendola A, Sampaolo S, Migliaresi S, Ambrosone L, Ammendola E, Ciccone G, et al. Autonomic neuropathy in mixed cryoglobulinemia. J Neurol. 2007;254(2):215–9. doi:10.1007/s00415-006-0333-6.View ArticlePubMedGoogle Scholar
- Osztovits J, Horvath T, Abonyi M, Toth T, Visnyei Z, Beko G, et al. Chronic hepatitis C virus infection associated with autonomic dysfunction. Liver Int. 2009;29(10):1473–8. doi:10.1111/j.1478-3231.2009.02075.x.View ArticlePubMedGoogle Scholar
- Osztovits J, Horvath E, Tax J, Csihi L, Horvath T, Littvay L, et al. Reversible autonomic dysfunction during antiviral treatment in patients with chronic hepatitis C virus infection: Anti-HCV therapy and autonomic function. Hepat Mon. 2011;11(2):114–8.PubMedPubMed CentralGoogle Scholar
- Zoccoli G, Andreoli E, Bojic T, Cianci T, Franzini C, Predieri S, et al. Central and baroreflex control of heart rate during the wake-sleep cycle in rat. Sleep. 2001;24(7):753–8.PubMedGoogle Scholar
- Silvani A, Bojic T, Cianci T, Franzini C, Lodi CA, Predieri S, et al. Effects of acoustic stimulation on cardiovascular regulation during sleep. Sleep. 2003;26(2):201–5.PubMedGoogle Scholar
- Bojić T. Mechanisms of cardiovascular control and effects of acoustic stimulation on cardiovascular system during the wake-sleep cycle [Dissertation]. Bologna: Alma Mater Università di Bologna; 2003.Google Scholar
- Bajic D, Loncar-Turukalo T, Stojicic S, Sarenac O, Bojic T, Murphy D, et al. Temporal analysis of the spontaneous baroreceptor reflex during mild emotional stress in the rat. Stress. 2010;13(2):142–54. doi:10.3109/10253890903089842.View ArticlePubMedGoogle Scholar
- Bojić T, Radak D, Putniković B, Alavantić D, Isenović ER. Methodology of monitoring cardiovascular regulation: baroreflex and central mechanisms of cardiovascular regulation. Vojnosanit Pregl. 2012;69(12):1084–90. doi:10.2298/VSP110707019B.View ArticlePubMedGoogle Scholar
- Bojić T, Sudar E, Mikhailidis DP, Alavantić D, Isenović ER. The role of G protein coupled receptor kinases in neurocardiovascular pathophysiology. Arch Med Sci. 2012;8(6):970–7. doi:10.5114/aoms.2012.32466.PubMedPubMed CentralGoogle Scholar
- Hendrickse MT, Thuluvath PJ, Triger DR. Natural history of autonomic neuropathy in chronic liver disease. Lancet. 1992;339(8807):1462–4.View ArticlePubMedGoogle Scholar
- Osadchii OE. Dynamics of the vagus effect on the cardiac rhythm during blockade of various subtypes of M-cholinoreceptors in cats. Ross Fiziol Zh Im I M Sechenova. 2003;89(2):161–72.PubMedGoogle Scholar
- Goldstain D. The Autonomic Nervous System in Health and Disease. 1st ed. New York: Marcel Dekker, Inc; 2001.Google Scholar
- Wang H, Lu Y, Wang Z. Function of cardiac M3 receptors. Auton Autacoid Pharmacol. 2007;27(1):1–11. doi:10.1111/j.1474-8673.2006.00381.x.View ArticlePubMedGoogle Scholar
- Budd DC, McDonald J, Emsley N, Cain K, Tobin AB. The C-terminal tail of the M3-muscarinic receptor possesses anti-apoptotic properties. J Biol Chem. 2003;278(21):19565–73. doi:10.1074/jbc.M211670200.View ArticlePubMedGoogle Scholar
- Cassiman D, Libbrecht L, Sinelli N, Desmet V, Denef C, Roskams T. The vagal nerve stimulates activation of the hepatic progenitor cell compartment via muscarinic acetylcholine receptor type 3. Am J Pathol. 2002;161(2):521–30. doi:10.1016/S0002-9440(10)64208-3.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferri S, Muratori L, Lenzi M, Granito A, Bianchi FB, Vergani D. HCV and autoimmunity. Curr Pharm Des. 2008;14(17):1678–85.View ArticlePubMedGoogle Scholar
- Bogdanos DP, Rigopoulou EI. Viral/self-mimicry and immunological cross-reactivity as a trigger of hepatic C virus associated autoimmune diabetes. Diabetes Res Clin Pract. 2007;77(1):155–6. doi:10.1016/j.diabres.2006.10.012.View ArticlePubMedGoogle Scholar
- Gregorio GV, Choudhuri K, Ma Y, Pensati P, Iorio R, Grant P, et al. Mimicry between the hepatitis C virus polyprotein and antigenic targets of nuclear and smooth muscle antibodies in chronic hepatitis C virus infection. Clin Exp Immunol. 2003;133(3):404–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Krsmanovic V, Biquard JM, Sikorska-Walker M, Cosic I, Desgranges C, Trabaud MA, et al. Investigations into the cross-reactivity of rabbit antibodies raised against nonhomologous pairs of synthetic peptides derived from HIV-1 gp120 proteins. J Pept Res. 1998;52(5):410–20.View ArticlePubMedGoogle Scholar
- Veljkovic N, Branch DR, Metlas R, Prljic J, Vlahovicek K, Pongor S, et al. Design of peptide mimetics of HIV-1 gp120 for prevention and therapy of HIV disease. J Pept Res. 2003;62(4):158–66.View ArticlePubMedGoogle Scholar
- Veljkovic N, Branch DR, Metlas R, Prljic J, Manfredi R, Stringer WW, et al. Antibodies reactive with C-terminus of the second conserved region of HIV-1gp120 as possible prognostic marker and therapeutic agent for HIV disease. J Clin Virol. 2004;31 Suppl 1:S39–44. doi:10.1016/j.jcv.2004.09.012.View ArticlePubMedGoogle Scholar
- Vergara-Alert J, Argilaguet JM, Busquets N, Ballester M, Martin-Valls GE, Rivas R, et al. Conserved synthetic peptides from the hemagglutinin of influenza viruses induce broad humoral and T-cell responses in a pig model. PLoS One. 2012;7(7):e40524. doi:10.1371/journal.pone.0040524.View ArticlePubMedPubMed CentralGoogle Scholar
- Combet C, Garnier N, Charavay C, Grando D, Crisan D, Lopez J, et al. euHCVdb: the European hepatitis C virus database. Nucleic Acids Res. 2007;35(Database issue):D363–6. doi:10.1093/nar/gkl970.View ArticlePubMedPubMed CentralGoogle Scholar
- Veljkovic V, Slavic I. Simple general-model pseudopotential. Phys Rev Lett. 1972;29:105–6.View ArticleGoogle Scholar
- Veljkovic V. The dependence of the Fermi energy on the atomic number. Phys Lett. 1973;45A:41–2.View ArticleGoogle Scholar
- Veljkovic V. Theoretical approach to preselection of cancerogens and chemical carcinogenesis. New York: Gordon & Breach; 1980.Google Scholar
- Veljkovic V, Cosic I, Dimitrijevic B, Lalovic D. Is it possible to analyze DNA and protein sequences by the methods of digital signal processing? IEEE Transactions on Biomedical Engineering. 1985;BME-32(5):337–41.View ArticleGoogle Scholar
- Veljkovic V, Cosic I. A novel method of protein analysis for prediction of biological function. Cancer Biochem Biophys. 1987;9:139.PubMedGoogle Scholar
- Anu SR, Tessamma T. A Frequency Domain Approach to Protein sequence Similarity Analysis and Functional Classification. Signal & Image Processing: An International Journal. 2011;2(1).Google Scholar
- Parker JL, Claesson PM, Attard P. Bubbles, cavities, and the long-ranged attraction between hydrophobic surfaces. J Phys Chem. 1994;98(34):8468–80.View ArticleGoogle Scholar
- Deschenes LA, Zilaro P, Muller LJ, Fourkas JT, Mohanty U. Quantitative Measure of Hydrophobicity: Experiment and Theory. J Phys Chem. 1997;101(30):5777–9.View ArticleGoogle Scholar
- Stevens H, CR F, Drummond CJ RAH, Attard P. Effects of Degassing on the Long-Range Attractive Force between Hydrophobic Surfaces in Water. Langmuir. 2005;21:6399–405.View ArticlePubMedGoogle Scholar
- Veljkovic N, Glisic S, Prljic J, Perovic V, Botta M, Veljkovic V. Discovery of new therapeutic targets by the informational spectrum method. Curr Protein Pept Sci. 2008;9(5):493–506.View ArticlePubMedGoogle Scholar
- Lazovic J. Selection of amino acid parameters for Fourier transform-based analysis of proteins. Comput Appl Biosci. 1996;12(6):553–62.PubMedGoogle Scholar
- Macindoe G, Mavridis L, Venkatraman V, Devignes MD, Ritchie DW. HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic Acids Res. 2010;38(Web Server issue):W445-9. doi: 10.1093/nar/gkq311.
- Ascher DB, Wielens J, Nero TL, Doughty L, Morton CJ, Parker MW. Potent hepatitis C inhibitors bind directly to NS5A and reduce its affinity for RNA. Sci Rep. 2014;4:4765. doi:10.1038/srep04765.View ArticlePubMedPubMed CentralGoogle Scholar
- Kang SM, Jun HJ, Lim YS, Choi SH, Hwang SB. Characterization of monoclonal antibodies against the nonstructural 5A protein of hepatitis C virus. Arch Virol. 2009;154(5):843–51. doi:10.1007/s00705-009-0386-9.View ArticlePubMedGoogle Scholar
- Issur M, Gotte M. Resistance patterns associated with HCV NS5A inhibitors provide limited insight into drug binding. Viruses. 2014;6(11):4227–41. doi:10.3390/v6114227.View ArticlePubMedPubMed CentralGoogle Scholar
- Love RA, Brodsky O, Hickey MJ, Wells PA, Cronin CN. Crystal structure of a novel dimeric form of NS5A domain I protein from hepatitis C virus. J Virol. 2009;83(9):4395–403. doi:10.1128/JVI.02352-08.View ArticlePubMedPubMed CentralGoogle Scholar
- Kruse AC, Hu J, Pan AC, Arlow DH, Rosenbaum DM, Rosemond E, et al. Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature. 2012;482(7386):552–6. doi:10.1038/nature10867.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen K, Huzil JT, Freedman H, Ramachandran P, Antoniou A, Tuszynski JA, et al. Identification of tubulin drug binding sites and prediction of relative differences in binding affinities to tubulin isotypes using digital signal processing. J Mol Graph Model. 2008;27(4):497–505. doi:10.1016/j.jmgm.2008.09.001.View ArticlePubMedGoogle Scholar
- Nagarajan V, Kaushik N, Murali B, Zhang C, Lakhera S, Elasri MO et al. A Fourier transformation based method to mine peptide space for antimicrobial activity. BMC Bioinformatics. 2006;7 Suppl 2:S2. doi: 10.1186/1471-2105-7-S2-S2.
- Mandell AJ, Selz KA, Owens MJ, Kinkead B, Shlesinger MF, Gutman DA, et al. Cellular and behavioral effects of D2 dopamine receptor hydrophobic eigenmode-targeted peptide ligands. Neuropsychopharmacology. 2003;28 Suppl 1:S98–107. doi:10.1038/sj.npp.1300134.View ArticlePubMedGoogle Scholar
- Barrera JM, Francis B, Ercilla G, Nelles M, Achord D, Darner J, et al. Improved detection of anti-HCV in post-transfusion hepatitis by a third-generation ELISA. Vox Sang. 1995;68(1):15–8.View ArticlePubMedGoogle Scholar
- Lee SR, Wood CL, Lane MJ, Francis B, Gust C, Higgs CM, et al. Increased detection of hepatitis C virus infection in commercial plasma donors by a third-generation screening assay. Transfusion. 1995;35(10):845–9.View ArticlePubMedGoogle Scholar
- Dow BC, Buchanan I, Munro H, Follett EA, Davidson F, Prescott LE, et al. Relevance of RIBA-3 supplementary test to HCV PCR positivity and genotypes for HCV confirmation of blood donors. J Med Virol. 1996;49(2):132–6.View ArticlePubMedGoogle Scholar
- Rodriguez-Lopez M, Riezu-Boj JI, Ruiz M, Berasain C, Civeira MP, Prieto J, et al. Immunogenicity of variable regions of hepatitis C virus proteins: selection and modification of peptide epitopes to assess hepatitis C virus genotypes by ELISA. J Gen Virol. 1999;80(Pt 3):727–38.View ArticlePubMedGoogle Scholar
- Koo NY, Li J, Hwang SM, Choi SY, Lee SJ, Oh SB, et al. Functional epitope of muscarinic type 3 receptor which interacts with autoantibodies from Sjogren’s syndrome patients. Rheumatology (Oxford). 2008;47(6):828–33. doi:10.1093/rheumatology/ken064.View ArticleGoogle Scholar
- Tsuboi H, Matsumoto I, Wakamatsu E, Nakamura Y, Iizuka M, Hayashi T, et al. New epitopes and function of anti-M3 muscarinic acetylcholine receptor antibodies in patients with Sjogren’s syndrome. Clin Exp Immunol. 2010;162(1):53–61.View ArticlePubMedPubMed CentralGoogle Scholar
- Ramos-Casals M, Loustaud-Ratti V, De Vita S, Zeher M, Bosch JA, Toussirot E, et al. Sjogren syndrome associated with hepatitis C virus: a multicenter analysis of 137 cases. Medicine (Baltimore). 2005;84(2):81–9.View ArticleGoogle Scholar
- Li J, Ha YM, Ku NY, Choi SY, Lee SJ, Oh SB, et al. Inhibitory effects of autoantibodies on the muscarinic receptors in Sjogren’s syndrome. Lab Invest. 2004;84(11):1430–8. doi:10.1038/labinvest.3700173.View ArticlePubMedGoogle Scholar
- Jin M, Hwang SM, Davies AJ, Shin Y, Bae JS, Lee JH, et al. Autoantibodies in primary Sjogren’s syndrome patients induce internalization of muscarinic type 3 receptors. Biochim Biophys Acta. 2012;1822(2):161–7. doi:10.1016/j.bbadis.2011.11.012.View ArticlePubMedGoogle Scholar