Abstract

Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria.GEMS performs bicluster mining based on a Gibbs sampling paradigm. Here, in Biogenea we have for the first time discovered an Anticancer Peptide SVS-1 multipharmacophore with an ini silico evaluated Efficacy in Preceding Membrane Neutralization using a web server for biclustering analysis of expression data.

Abstract

The ever-increasing number of tumor-associated antigens has provided a major stimulus for the development of therapeutic peptides vaccines. Tumor-associated peptides can induce high immune response rates and have been developed as vaccines for several types of solid tumors, and many are at various stages of clinical testing. MAGED4B, a melanoma antigen, is overexpressed in oral squamous cell carcinoma (OSCC) and this expression promotes proliferation and cell migration. In previous scientifc projects it has also been identified that 9 short peptides derived from MAGED4B protein are restricted in binding to the HLA subtypes common in the Asian population (HLA-A2, A11, and A24). As a result, we here discovered for the first time the GENEA-Immunomagetor-45700d utilizing the KNIME-BiogenetoligandorolTM-PASS-KNIME-based GA(M)E-QSAR-FIPSDock: a new molecular docking combinatorial clustering technique driven by fully informed swarm optimization algorithm and GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design based on Chemical and biological properties of frequent screening hits in predicting drug targets based on conserved binding pocket active MAGED4B protein domains.

Abstract

Keloids result from aberrations in the normal wound healing cascade and can lead to pruritus, contractures and pain. The underlying mechanisms of excessive scarring are not yet understood, and most therapeutic strategies remain unsatisfactory. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. S100 production is markedly decreased in keloid scar tissue. The disturbed epidermal S100 expression might contribute to keloid formation; thus, it has been previously studied their effect on dermal fibroblasts and extracellular matrix (ECM) production. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-AntiPsorerisin-10715. An in silico rational computer-aided designed of Antimicrobial Peptides Psoriasin (S100A7) and Koebnerisin (S100A15) mimetic pharmacophore for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts by Predicting interacting residues using long-distance information and novel decoding in hidden Markov models.

Abstract

Poor cellular delivery and low bioavailability of novel potent therapeutic molecules continue to remain the bottleneck of modern cancer and gene therapy. Cell-penetrating peptides have provided immense opportunities for the intracellular delivery of bioactive cargos and have led to the first exciting successes in experimental therapy of muscular dystrophies. The arsenal of tools for oligonucleotide delivery has dramatically expanded in the last decade enabling harnessing of cell-surface receptors for targeted delivery.A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. These prediction rates are much higher than the 11.11% achieved by random guessResearch and Scientific Project. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-Delivernarex-3308. An In silico rationally designed of a potential Peptide-mimic pharmacologicchemorecored poly-chemo-structure as a possible future synthetic ligand for the delivery of gene constructs through for efficient internalization utilizing the BiogeneotligandorolTM and the HotLig: a molecular surface-directed approach to scoring protein-ligand interactions through a novel Prediction Methodology of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections.

Abstract

Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Targeting pathogenic T cells using vaccines consisting of synthetic peptides representing T cell epitopes is one such strategy that is currently being evaluated with encouraging results. Future challenges in the development of therapeutic vaccines include selection of appropriate antigens and peptides, optimization of peptide dose and route of administration and identifying strategies to induce bystander suppression. Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given peptide based on their structural complementarity. Compared to other peptide and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. Here, in Biogenea we have for the first time generated a surface representation based Peptidomimetic vaccine-like annotated pharmacostructure for allergic and autoimmune diseases using PL-PatchSurfer in a BiogenetoligandorolTM clustering algorithmic enviroment.

Abstract

In silico Drug discovery and development of novel multi-target molecules is an interdisciplinary, expensive and time-consuming procedure. Computer aided drug discovery advancements during the past decades have improved the way of pharmaceutical research design of novel bioactive huper-structured drug-gable molecules. Computer aided drug design helps in reducing the cost and time for drug discovery process which otherwise takes many years. Virtual screening and docking studies helped to obtain ligand molecules that can inhibit the important Proteins involved in the pathogenesis of Ebola virus. It is noticed that the chemical compounds might be the promising candidates drug-like small targeted compounds for further pre-clinical and clinical investigation, and that the NP and the octapeptides ATLQAIAS and ATLQAENV, as well as AVLQSGFR, might be pre-clinically translated and antisense converted to effective direct inhibitors against the Ebola Virus Fusion Conserved Proteoma. Meanwhile, we in silico generated conserved octapeptides mimotopic pharmaco-ligands based on the “distorted key energy binding fitness scoring” theory to in-silico anti-sense peptides by in-silico translate them and transform them into a scaffold energy hopping structure in order to design potent selective super-agonsist anti-peptide poly-mimic new superstructure which is explicitly elucidated. We also combined all existing methods for computational huper-structured drug design methodologies to induce catalysis of Ebola Virus EBOV NP and EBO16 peptides by inducing energetically targeted favorable hydrogen bonds, van der Waals, and electrostatic interactions to a high-energy reaction conserved motif-based transition state(s) and/or intermediate(s) of Ebola virus. In this present Research Scientific Project , for first time we developed a computational method for designing motif-like conserved residues and ligand binding virus proteins with two properties characteristic of naturally occurring binding sites in addition to specific energetically favorable interactions with our newly designed hyper-multi-target ligand. Here, in Biogenea we have in silico discovered a virion-attached pharmacophore cross-reacting synthetic EQHHRRTDN peptide mimetic ligand comprising potential therapeutic properties against Ebola virus using an in silico drug design structure peptide-sequence-based combinatorial analysis by a multi-objective cluster of algorithms.

Abstract

We have developed a novel class of multivalent tubulin mimotopic VDAC-1 peptide like chemical inhibitor assembly by nano-chemical HA14-1 modifying scaffold, which is a Bcl-2 inhibitor discovered in previous scientific projects. Cell-penetrating VDAC1-based peptides were designed and screened to identify the most stable, short and apoptosis-inducing peptides toward CLL-derived lymphocytes revealing the potential of VDAC1-based peptides as an innovative and effective anti-CLL therapy. Multivalency is a design principle that can convert inhibitors with low affinity to ones with high avidity and/or biological “activity” gauged by some relevant parameter: (for example, values of IC50 the concentration of free ligand, often approximated as the total ligand, that reduces the experimental signal to 50% of its initial value). In addition, multivalent approaches can be effective in generating high-avidity ligands for proteins with multiple binding sites from low-affinity ligands. Multivalent ligands (primarily polyvalent ones) are especially well suited for inhibiting or augmenting interactions at biological surfaces (e. g., surfaces of bacteria, viruses, cells they can prevent adhesion of these surfaces to other surfaces by grafting polymers to the surfaces of viruses to prevent adhesion to cells). Computational docking, colchicine-tubulin competitive binding, and tubulin polymerization studies demonstrated that these compounds bind at the colchicine-binding site on tubulin and inhibit the formation of microtubules. The mode of action of the VDAC-1 peptides involves dysfunction of mitochondria energy production and apoptosis induction. In this study, we confine attention to the so called ligand-based target prediction machine learning peptide mimetic drug discovery approaches, commonly referred to as drug target fishing. These results demonstrate that the VDAC1 treating CLL peptides may assist target-fishing approaches that are currently ubiquitous in cheminformatics and can be essentially viewed as single-label peptidomimetic drug discovery schemes. Here, we have for the first time in silico discovered a Novel Class of VDAC1-peptide mimetic Tubulin targeted HA14-1-based multivalent chemical Inhibitor with Promising Anticancer Activities as novel pro-apoptotic annotated agent for B-cell chronic lymphocytic leukemia.

Abstract

Glycoprotein-96, a non-polymorphic heat-shock protein, associates with intracellular peptides. Autologous tumor-derived heat shock protein-peptide complex 96 (HSPPC-96) can elicit potent tumor-specific T cell responses and protective immunity in animal models. Chemokines were described originally in the context of providing migrational cues for leukocytes. They are now known to have broader activities, including those that favor tumor growth. Treatment with autologous tumor-derived HSPPC-96 was feasible and safe at all doses tested. Observed immunological effects and antitumor activity were modest, precluding selection of a biologically active dose. Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this Research Scientific Project, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on thecomplex-96 (HSPPC-96) protein complex to evaluate coevolution between conserved binding protein domain regions. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37complex-96 (HSPPC-96) binding domains conserved peptide derived residues and its fitness scoring reverse ligand docking interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in thecomplex-96 (HSPPC-96) complex can be helpful for designing protein drug target and in silico discovery of engineering novel nanomolecule experiments. In this scientific study we have in silico discovered a Unique Small Molecule Modulator of CXCR4 tumor-derived heat-shock protein peptide complex-96 (HSPPC-96) by identifying Hits of a High-Throughput Screen Identify the Hydrophobic Pocket of Autotaxin/Lysophospholipase D as an Inhibitory Surface Molecular dynamic simulation and statistical coupling analysis via a KNIME-based BiogenetoligandorolTM generated of a functional coevolution network of oncogenic mutations in the (HSPPC-96) hyper-interactive multicovalent annotated pharmacophore construct complexes.

Abstract

The effectivenes of cancer vaccines in inducing CD8+Tcell responses remains a challenge, resulting in a need for testing more potent adjuvants. In previous clinical trials it has been determined the safetyand immunogenicity of vaccination against melanoma-related antigens employing MART-1,gp100, and tysosinase paptides combined with the TLR-9 agonist PF-3512676 and local GM-CSFin-oil emulsion.Using continuous monitoring of safety and a two-stage design for immunological efficacy, More than 20 immune-response evaluable patients were targetted. Vaccinations were given subcutaneously ondays 1 and 15 per cycle (1 cycle=28 days) for up to 13 cycles. Structure-based virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. That said, virtual screening is heavily dependent on detailed understanding of the tertiary or quaternary structure of the protein target of interest, including knowledge of the relevant binding pocket. Here, in Biogenea we have for the first time discovered a Safe and immunogenic pharmacophore activator mimic physicochemical properties of the MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) inadjuvantwith PF-3512676 and GM-CSF as a future anti-cancer agent in metastatic melanoma conditions introducing a novel multi-parametric algorithm drug discovery approach using a Ligand-Based Virtual Screening approach through a Support Vector Machine and Information Fusion attempt.

Abstract

Animals and higher plants express endogenous peptide antibiotics called defensins. These small cysteine-rich peptides are active against bacteria, fungi and viruses. Plectasin—the first defensin has been isolated from a fungus, the saprophytic ascomycetePseudoplectania nigrella. Polypeptides having antimicrobial activity may be capable of reducing the number of living cells of Bacillus subtilis (ATCC 6633). Plectasin has primary, secondary and tertiary structures that closely resemble those of defensins found in spiders, scorpions, dragonflies and mussels. Recombinant plectasin was produced at a very high, and commercially viable, yield and purity. In vitro, the recombinant peptide was especially active againstStreptococcus pneumoniae, including strains resistant to conventional antibiotics. Plectasin showed extremely low toxicity in mice, and cured them of experimental peritonitis and pneumonia caused by S. pneumoniae as efficaciously as vancomycin and penicillin. These findings identify fungi as a novel source of antimicrobial defensins, and show the therapeutic potential of plectasin. They also suggest that the defensins of insects, molluscs and fungi arose from a common ancestral gene. The Poisson-Boltzmann equation models the electrostatic potential generated by fixed charges on a polarizable solute immersed in an ionic solution. This approach is often used in computational structural biology to estimate the electrostatic energetic component of the assembly of molecular biological systems. In the last decades, the amount of data concerning proteins and other biological macromolecules has remarkably increased. To fruitfully exploit these data, a huge computational power is needed as well as software tools capable of exploiting it. It is therefore necessary to move towards high performance computing and to develop parallel implementations of already existing and of novel algorithms. In this Research and Scientific Project, we propose the implementation of a full Poisson-Boltzmann solver based on a finite-difference scheme using different and combined parallel schemes and in particular a mixed MPI-CUDA implementation. Here, we have for the first time discovered a A Rational predicted pDRS-18-plectasin peptide-mimetic pharmacophore comprising antibiotic properties with therapeutic potential from a saprophytic fungus using BiogenetoligandorolTM drug discovery process combined MPI-CUDA parallel solution of linear and nonlinear Poisson-Boltzmann equation.