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Immune Response to Controlling Ebola Virus Infection

Organized by: Chai Ezerzer

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Immune Response to controlling Ebola Virus Infection


Current Focus:

Ebola Virus Disease (EVD):

Ebolavirus (EBOV) and Marburgvirus (MARV) are single-stranded, negative-sense RNA
viruses of the Filoviridae family recognized for their impressive lethality. Filoviruses are causative agents of viral hemorrhagic fever (VHF) characterized by low blood pressure, inflammation, abnormally low level of lymphocytes in the blood, relative decrease of platelets in the blood, bleeding disorders, blood escaping from the circulatory system, and a uncontrolled cytokine-storm response resulting in multi-organ failure and death.

Aim I: Accelerate the development of therapies – using In-Silico analysis

Convincingly, much of the undesirable inflammation that characterizes Ebola Virus infection (EVI) results, from aberrant regulation of the multi-sets of interactions of otherwise normal molecules, resulting from malfunctioning networks.

Systems Biology: the appreciation that living organisms can be better understood and manipulated as dynamic systems, rather than as collections of individual molecules. The collections of individual molecules are the Ebola Associated Proteins (EAPs) that can also be related by shared amino acid sequence motifs giving rise to Sequence Sharing networks (SSNs) relationships; SSNs extend the potential links between molecules and highlight new molecules that might be functionally important in the multi-network systems.

Drug discovery algorithm: estimated time of completion (ETC): about 2-3 months

Accelerated drug discovery-development program, can be generated rapidly and efficiently by using an In-Silico Analysis of all the EAPs based on experimental data and clinical results, publicly available, to identify potential therapeutic immune modulators and Ebola’s proteins binding peptides (motifs). The particular SSN motifs are based on the presence of many different multi-sets of Ebola proteins and immune response, elevated secretion of inflammatory proteins to Ebola infection.

SymThera’s analytical algorithm aligns multiple proteins to determine how they are related with respect to amino acid content and amino acid sequence. Whereas the conventional algorithm determines the overall relatedness of multiple proteins, the In-Silico analysis identifies and defines discrete "consensus sequences" common to members of a protein family, or shared by different protein families. The In-Silico analysis operates on the principle that amino acid sequences that are unique to a particular protein family, or are common to distinct protein families, are the ones that are essential for the proteins' structure and perforce, function. In-Silico analysis-generate short sequences of amino acid motifs, critical for protein integrity and activity and as such, prime targets for pharmacological modification. The algorithm, therefore, not only identifies and defines the pharmacophore, but predicts the location and sequence of its target in the infection-related sets of proteins / immune-related sets of proteins. The identify putative motifs, that is, the SSNs for either infected or immune related sets of proteins in relation to the EVI will be synthesized chemically.

Irrespective of the actual mechanisms of action responsible for the observations of an infected individual with Ebola virus, the findings suggest that the search for novel sequence motifs could lead to the discovery of new ways to modulate the multi-complex networks of distributed connections that orchestrate infection and inflammation; EAPs are distributed within many clusters of proteins, family related and unrelated, found in various organ systems.

Aim II: Preventing further infection of the Ebola virus.

Irrespective of the actual mechanisms of action of Ebola - viruses with compact genomes of only 7 genes. Each of the 7 proteins is critical, many perform multiple functions, and some actually rearrange their structures to achieve those new functions. SymThera’s In-Silico analysis of the 7 different proteins of the different Ebola strains, BDBV, EBOV, RESTV and others, will identify and classify novel sequence motifs that could lead to the discovery of new ways to modulate the multi-complex networks of distributed functionality and the connections that orchestrate infection. As a result, these sequence-sharing motifs will affect the virus infectivity level by competing and changing the ratio of complexity of the viral proteins and hence affect there biological functions; immune evasion, receptor recognition, cell entry, transcription, translation, assembly and exit.

SymThera occupies a unique niche in drug discovery by being the first company to exploit network theory to identify patterns of conserved functionality in groups of disease and infection-associated proteins known to control disease-specific inflammation and infection-specific to pathogens. Using our network theory, SymThera expects to deliver a series of high-value development compounds for the treatment of Ebola infection. SymThera also expects to partner with pharmaceutical companies wishing to use our services to apply the In-Silico algorithm to their proprietary sets of targets.


Organized by

Chai Ezerzer

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