Investigations into the cross-reactivity of rabbit antibodies raised against nonhomologous pairs of synthetic peptides derived from HIV-1 gp120 proteins

RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new coronavirus causing a worldwide pandemic. It is infecting respiratory organs and, in more severe cases, the lungs, where it is infecting the human cells through the angiotensin-converting enzyme 2 (ACE2) receptor. In severe cases, it is characterized not only by difficulties in breathing through infected lungs, but also with disproportionally and, thus far, unexplained low levels of oxygen in the blood. Here, we propose that, besides the infection of respiratory organs through ACE2 receptors, there is an additional infection in the red blood cells (erythrocytes). There could be a possible for SARS-CoV-2 to pass through the alveoli membrane in the lungs and infect the red blood cells through another receptor. Using our own biophysical model, the Resonant Recognition Model, we propose that the red blood cell (RBC) Band3 protein on the surface of red blood cells is a possible entry point for the SARS-CoV-2 virus into red blood cells.

Analysis of Protein–Receptor Interactions on an Example of Leptin–Leptin Receptor Interaction Using the Resonant Recognition Model

Obesity is a medical condition in which excess body fat may have a negative effect on health and lifestyle, and it is becoming an increasing problem within modern society. Leptin is the key protein that regulates body energy balance by inhibiting hunger, and it could potentially be used in treatment of obesity and overweight. Here, we applied our own Resonant Recognition Model, which is capable of analyzing the selectivity of any protein–receptor interaction on an example of leptin–leptin receptor. We have identified a specific characteristic parameter for leptin activity through the leptin receptor, and this parameter could be used in development of new treatments for obesity.

Analysis of Tumor Necrosis Factor Function Using the Resonant Recognition Model

The tumor necrosis factor (TNF) is a complex protein that plays a very important role in a number of biological functions including apoptotic cell death, tumor regression, cachexia, inflammation inhibition of tumorigenesis and viral replication. Its most interesting function is that it is an inhibitor of tumorigenesis and inductor of apoptosis. Thus, the TNF could be a good candidate for cancer therapy. However, the TNF has also inflammatory and toxic effects. Therefore, it would be very important to understand complex functions of the TNF and consequently be able to predict mutations or even design the new TNF-related proteins that will have only a tumor inhibition function, but not other side effects. This can be achieved by applying the resonant recognition model (RRM), a unique computational model of analysing macromolecular sequences of proteins, DNA and RNA. The RRM is based on finding that certain periodicities in distribution of free electron energies along protein, DNA and RNA are strongly correlated to the biological function of these macromolecules. Thus, based on these findings, the RRM has capabilities of protein function identification, prediction of bioactive amino acids and protein design with desired biological function. Using the RRM, we separate different functions of TNF as different periodicities (frequencies) within the distribution of free energy electrons along TNF protein. Interestingly, these characteristic TNF frequencies are related to previously identified characteristics of proto-oncogene and oncogene proteins describing TNF involvement in oncogenesis. Consequently, we identify the key amino acids related to the crucial TNF function, i.e. receptor recognition. We have also designed the peptide which will have the ability to recognise the receptor without side effects.

Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model

The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM). The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1) the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2) the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3) the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4) the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

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

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

MSCA: a spectral comparison algorithm between time series to identify protein-protein interactions

Background

The interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive. Therefore, we develop a statistical method based on hypothesis testing to compare the time series obtained from conversion of the physicochemical characteristics of the amino acids that form the primary structure of proteins and thus to propose potential functional relation between proteins. We called this algorithm the multiple spectral comparison algorithm (MSCA); the MSCA was inspired by the BLASTP tool and was implemented in R code. The algorithm compares and relates multiple time series according to their spectral similarities, and the biological relation between them could be interpreted as either a similar function or protein-protein interaction (PPI).

Results

A simulation study showed that the MSCA works satisfactorily well when we compare unequal time series generated from ARMA processes because its power was close to 1. The MSCA presented a 70% average accuracy of detecting protein interactions using a threshold of 0.7 for our spectral measure, indicating that this algorithm could predict novel PPIs and pathogen-host interactions (PHIs) with acceptable confidence. The MSCA also was validated by its identification of well-known interactions of the human proteins MAGI1, SCRIB and JAK1, as well as interactions of the virulence proteins ROP16, ROP18, ROP17 and ROP5. We verified the spectral similarities for human intraspecific PPIs and PHIs that were previously demonstrated experimentally by other authors. We suggest that human GBP (GTPase group induced by interferon) and the CREB transcription factor family could be human substrates for the complex of ROP18, ROP17 and ROP5.

Conclusions

Using multiple-hypothesis testing between the spectral densities of a set of unequal time series, we developed an algorithm that is able to identify the similarities or interactions between a set of proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0599-8) contains supplementary material, which is available to authorized users.

Biological effects of a de novo designed myxoma virus peptide analogue: evaluation of cytotoxicity on tumor cells

Background

The Resonant Recognition Model (RRM) is a physico-mathematical model that interprets protein sequence linear information using digital signal processing methods. In this study the RRM concept was employed for structure-function analysis of myxoma virus (MV) proteins and the design of a short bioactive therapeutic peptide with MV-like antitumor/cytotoxic activity.

Methodology/Principal Findings

The analogue RRM-MV was designed by RRM as a linear 18 aa 2.3 kDa peptide. The biological activity of this computationally designed peptide analogue against cancer and normal cell lines was investigated. The cellular cytotoxicity effects were confirmed by confocal immunofluorescence microscopy, by measuring the levels of cytoplasmic lactate dehydrogenase (LDH) and by Prestoblue cell viability assay for up to 72 hours in peptide treated and non-treated cell cultures. Our results revealed that RRM-MV induced a significant dose and time-dependent cytotoxic effect on murine and human cancer cell lines. Yet, when normal murine cell lines were similarly treated with RRM-MV, no cytotoxic effects were observed. Furthermore, the non-bioactive RRM designed peptide RRM-C produced negligible cytotoxic effects on these cancer and normal cell lines when used at similar concentrations. The presence/absence of phosphorylated Akt activity in B16F0 mouse melanoma cells was assessed to indicate the possible apoptosis signalling pathway that could be affected by the peptide treatment. So far, Akt activity did not seem to be significantly affected by RRM-MV as is the case for the original viral protein.

Conclusions/Significance

Our findings indicate the successful application of the RRM concept to design a bioactive peptide analogue (RRM-MV) with cytotoxic effects on tumor cells only. This 2.345 kDa peptide analogue to a 49 kDa viral protein may be suitable to be developed as a potential cancer therapeutic. These results also open a new direction to the rational design of therapeutic agents for future cancer treatment.

A bioinformatics approach to identify natural autoantibodies from healthy blood donors' sera reactive with the HCV NS5A-derived peptide by immunoassay

Natural autoantibodies (NAbs) are continually produced throughout life and have an ability to recognize self and altered self, as well as foreign antigens, by recognizing cellular pattern recognition receptors. Sometimes NAb specificity demonstrates overlap between human and pathologic proteomes. This information can be useful in selecting target sequences for screening purposes. In this study we undertook a multi-step bioinformatics search to predict a virus-derived peptide that can be recognized by NAbs in sera of uninfected individuals. We selected protein hepatitis C virus (HCV) NS5A as a target sequence, motivated by the fact that the HCV proteome is characterized by extensive sequence similarities to the human proteome, and because screening for anti-HCV antibodies, including anti-NS5A, is important clinically, particularly in screening of potential blood donors. The virus-specific peptide P1, and the homologous human peptide derived from enzyme-inducible nitric oxide synthase (iNOS), P2, exhibiting not only simple homology, but also complementarities of physicochemical patterns, were synthesized and 80 HCV-negative and 50 HCV-positive blood donor sera were tested by ELISA. These peptides reacted similarly (p<0.001) with HCV-negative sera, and in several cases the measured reactivity was significantly above the cut-off value of commercial anti-HCV screening assays. In HCV-positive sera, the titers of antibodies reactive with analyzed HCV NS5A peptide were not significantly increased (p<0.001) compared to host peptide, the implications of which are unclear, but may be consistent with these antibodies being "naturally produced." Finally, we extended our bioinformatics analyses to the dataset of human self-binding sequences, and propose a general approach for the selection of specific diagnostic and screening antigens for use in immunoassays.

Investigating the interaction between oncogene and tumor suppressor protein

It is known that cancer develops when cells in a part of the body begin to grow out of control. Because cancer cells continue to grow and divide with no order, they never differentiate into the specific tissue, and thus, they are functionally different from normal cells. However, there are some genes that help to prevent cells' malignant behavior, and therefore, are referred to as tumor suppressor genes. Here, we have investigated the structural and functional relationships of p53, oncogene and interleukin 2 (IL2) proteins using the resonant recognition model (RRM), a physico-mathematical approach based on digital signal processing methods. In addition, using the RRM concepts, we have designed the peptide analoges that would exhibit tumor-suppression-like activity and be used in anticancer vaccine development.

Induction of human immunodeficiency virus (HIV-1) envelope specific cell-mediated immunity by a non-homologous synthetic peptide

Background

Cell mediated immunity, including efficient CTL response, is required to prevent HIV-1 from cell-to-cell transmission. In previous investigations, we have shown that B1 peptide derived by Fourier transformation of HIV-1 primary structures and sharing no sequence homology with the parent proteins was able to generate antiserum which recognizes envelope and Tat proteins. Here we have investigated cellular immune response towards a novel non-homologous peptide, referred to as cA1 peptide.

Methodology/Principal Findings

The 20 amino acid sequence of cA1 peptide was predicted using the notion of peptide hydropathic properties; the peptide is encoded by the complementary anti-sense DNA strand to the sense strand of previously described non-homologous A1 peptide. In this report we demonstrate that the cA1 peptide can be a target for major histocompatibility complex (MHC) class I-restricted cytotoxic T lymphocytes in HIV-1-infected or envelope-immunized individuals. The cA1 peptide is recognized in association with different MHC class I allotypes and could prime in vitro CTLs, derived from gp160-immunized individuals capable to recognize virus variants.

Conclusions/Significance

For the first time a theoretically designed immunogen involved in broad-based cell-immune memory activation is described. Our findings may thus contribute to the advance in vaccine research by describing a novel strategy to develop a synthetic AIDS vaccine.

Localization of recognition site between transforming growth factor- (TGF-) and TGF receptor type II: possible implications in breast cancer

Although overexpression of TGF-beta1 protein has been demonstrated in advanced breast cancer (BC) patients, as well as in other solid tumours, the molecular mechanism of this process remains obscure. This paper proposes that a genetic/epigenetic alteration might occur in the TGF-beta1 gene, within the region coding for the recognition site with TGFbeta receptor type II, leading to a disruption of the ligand-receptor interaction and triggering the TGF-beta1 cascade-related BC progression. To establish the operational framework for this hypothesis, in the present study, this recognition site was identified by the Informational Spectrum Method (ISM) to comprise two TGF-beta1 peptides (positions 47-66 aa and 83-112 aa) and one receptor peptide at positions 112-151 aa of the extracellular domain of the receptor (TbetaRIIM). The TbetaRIIM locus was further evaluated by ISM-derived deletion analysis of the TbetaRII sequences. To provide experimental support for the proposed model, a pilot study of plasma TGF-beta1 analysis was performed in advanced BC patients (n = 8). Two commercial ELISA assays, one with specific alphaTGF-beta1 MAb (MAb) and other with TbetaRIIM as the immobilized phase, revealed pronounced differences in the pattern of plasma TGF-beta1 elevation. In MAb-profile, the TGF-beta1 increase was detected in 7 of 8 patients, whereas analogous TbetaRIIM-profile revealed the elevation in 3 of 8 patients, taking a 50% of maximal elevation as the cut-off value. These findings are consistent with the proposed aberration of TGF-beta1 ligand within the TbetaRII recognition site. Summarizing, this model system is a good starting point for further genetic studies, particularly on genetic/epigenetic alterations of sequences involved in TGF-beta1 and TbetaRIIM interaction, with putative prognostic value for breast cancer.

Design of peptide mimetics of HIV-1 gp120 for prevention and therapy of HIV disease

It has been reported that the C-terminus of the second conserved region (C2) of the envelope glycoprotein gp120, encompassing peptide RSANFTDNAKTIIVQLNESVEIN (NTM), is important for infectivity and neutralization of the human immunodeficiency virus type 1 (HIV-1). It was also demonstrated that human natural anti-vasoactive intestinal peptide (VIP) antibodies reactive with this gp120 region play an important role in control of HIV disease progression. The bioinformatic analysis based on the time-frequency signal processing revealed non-obvious similarities between NTM and VIP. When tested against a battery of sera from 46 AIDS patients, these peptides, in spite of a significant difference in their primary structures, showed a similar reactivity profiles (r = 0.83). Presented results point out that similarity in the periodical pattern of some physicochemical properties in primary structures of peptides plays a significant role in determination of their immunological crossreactivity. Based on these findings, we propose this bioinformatic criterion be used for design of VIP/NTM peptide mimetics for prevention and treatment of HIV disease.

Investigation of the applicability of dielectric relaxation properties of amino acid solutions within the resonant recognition model

The resonant recognition model (RRM) is a physicomathematical approach used to analyze the interactions of a protein and its target, using digital signal processing methods. The RRM is based on the finding that there is a significant correlation between the spectra of numerical presentation of protein sequences and their biological activities. Initially, the electron-ion interaction potential was used to represent each amino acid in the protein sequences. In this paper, the dielectric constant (epsilon') and dielectric loss tangent (tan delta) parameters have been determined for their possible use in the RRM. These parameters are based on the values of capacitance and conductance obtained experimentally for 20 amino acid solutions using dielectric spectroscopy for the case of the real component of dielectric permittivity; the parameter used is the dielectric increment (deltaepsilon'), the difference between dielectric constant of the amino acid solution and that of the solvent alone. The results of multiple cross-spectral analyses have shown that parameters analyzed generate in the consensus spectrum one dominant peak corresponding to the common biological activity of proteins studied, allowing the conclusion that these new parameters are suitable for use in the RRM approach.