Structural and Functional Modeling of Artificial Bioactive Proteins

Antisense Peptide Technology for Diagnostic Tests and Bioengineering Research

Antisense peptide technology (APT) is based on a useful heuristic algorithm for rational peptide design. It was deduced from empirical observations that peptides consisting of complementary (sense and antisense) amino acids interact with higher probability and affinity than the randomly selected ones. This phenomenon is closely related to the structure of the standard genetic code table, and at the same time, is unrelated to the direction of its codon sequence translation. The concept of complementary peptide interaction is discussed, and its possible applications to diagnostic tests and bioengineering research are summarized. Problems and difficulties that may arise using APT are discussed, and possible solutions are proposed. The methodology was tested on the example of SARS-CoV-2. It is shown that the CABS-dock server accurately predicts the binding of antisense peptides to the SARS-CoV-2 receptor binding domain without requiring predefinition of the binding site. It is concluded that the benefits of APT outweigh the costs of random peptide screening and could lead to considerable savings in time and resources, especially if combined with other computational and immunochemical methods.

The temperature dependence of amino acid hydrophobicity data is related to the genetic coding algorithm for complementary (sense and antisense) peptide interactions

We present the data concerning the clustering of sense and antisense amino acid pairs into polar, nonpolar and neutral groups, as measured using hydrophobicity parameter-logarithmic equilibrium constants (Log₁₀ K_w>c)-at 25 °C and 100 °C (Wolfenden et al., 2015). The Log₁₀ K_w>c, values, of the complementary amino acid pairs are strongly correlated to the central (2nd) purine base of the mRNA codon and the complementary pyrimidine base of the tRNA anticodon. Clustering of amino acids is temperature independent with regard to the direction of translation (3' → 5' or 5' → 3'). The Log₁₀ K_w>c discriminate between artificial Hecht α- and β-protein datasets at 25 °C and 100 °C. Interpretation of this data may be found in the research article entitled "Determining amino acid scores of the genetic code table: complementarity, structure, function and evolution" (Štambuk and Konjevoda, 2020).

Targeting Tumor Markers with Antisense Peptides: An Example of Human Prostate Specific Antigen

The purpose of this paper was to outline the development of short peptide targeting of the human prostate specific antigen (hPSA), and to evaluate its effectiveness in staining PSA in human prostate cancer tissue. The targeting of the hPSA antigen by means of antisense peptide AVRDKVG was designed according to a three-step method involving: 1. The selection of the molecular target (hPSA epitope), 2. the modeling of an antisense peptide (paratope) based on the epitope sequence, and 3. the spectroscopic evaluation of sense–antisense peptide binding. We then modified standard hPSA immunohistochemical staining practice by using a biotinylated antisense peptide instead of the standard monoclonal antibody and compared the results of both procedures. Immunochemical testing on human tissue showed the applicability of the antisense peptide technology to human molecular targets. This methodology represents a new approach to deriving peptide ligands and potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines.