Structural and Functional Modeling of Artificial Bioactive Proteins

Abstract

A total of 32 synthetic proteins designed by Michael Hecht and co-workers was investigated using standard bioinformatics tools for the structure and function modeling. The dataset consisted of 15 artificial α-proteins (Hecht_α) designed to fold into 102-residue four-helix bundles and 17 artificial six-stranded β-sheet proteins (Hecht_β). We compared the experimentally-determined properties of the sequences investigated with the results of computational methods for protein structure and bioactivity prediction. The conclusion reached is that the dataset of Michael Hecht and co-workers could be successfully used both to test current methods and to develop new ones for the characterization of artificially-designed molecules based on the specific binary patterns of amino acid polarity. The comparative investigations of the bioinformatics methods on the datasets of both de novo proteins and natural ones may lead to: (1) improvement of the existing tools for protein structure and function analysis; (2) new algorithms for the construction of de novo protein subsets; and (3) additional information on the complex natural sequence space and its relation to the individual subspaces of de novo sequences. Additional investigations on different and varied datasets are needed to confirm the general applicability of this concept.

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

“…This finding is confirmed by different computational methods. Table 2 shows that the consensus of 10 epitope prediction servers, based on different physicochemical criteria [27,28,29,30,31,32,33,34,35,36,37,38,39,40], also points to the RHSLFHP sequence as the most probable hPSA epitope [23,24,25,26]. The same result is obtained using binding site prediction methods: Phyre2–3DLigandSite and RaptorX (Figure 1) [41,42,43,44].…”

“…Tryptophan fluorescence spectroscopy and microscale thermophoresis are simple, quick, inexpensive and could be used for high-throughput screening [19,20,49,51,52]. The combination of these methods with computational procedures for ligand-acceptor modeling and design enables the quick and simple selection of the antisense peptide structures, which can be used for further development [19,20,27].…”

“…In silico prediction of the linear hPSA epitope RHSLFHP was carried out using the consensus result of 10 servers presented in Table 2: ExPASy–ProtScale (Kyte & Doolittle hydropathy scale, window size 9), BcePred (antigenic propensity method), BepiPred-2 (random forest regression algorithm), LBtope (support vector machine models), SCRATCH–COBEpro (support vector machine based propensity score; Scheme S1), Sann and RVP-net (solvent accessibility algorithms), NetTurnP and LEPS (β-turn determination) and the informational spectrum method based on electron-ion interaction potential (ISM-EIIP) [27,28,29,30,31,32,33,34,35,36,37,38,39,40]. The hPSA sequence is given in Scheme S1.…”

Antisense Peptide Technology for Diagnostic Tests and Bioengineering Research

“…Additionally, it was also shown that modern computational methods enable a new approach to the studies of sense and antisense peptide interactions [20]. Several free web-based services for protein structure prediction and modeling (e.g., I-TASSER, Phyre2, PEP-FOLD 3, CABS-dock) enable accurate proteinpeptide docking, i.e., in silico search for the peptide binding sites [20,28,80,86,87,[89][90][91].…”

“…An APT-based approach is also useful for peptide interaction and pharmacophore modeling [32,35]. The application of artificial proteins in the context of APT is also a plausible method to derive new antisense modulators of the protein interactions [19,24,88,91,92].…”