To be cost-effective, biomedical proteins must be optimized with regard to many factors. Road maps are customary for large-scale projects, and here descriptive methods based on bioinformatic fractal thermodynamic scales are tested against an important example, HPV vaccine. Older scales from before 2000 are found to yield inconclusive results, but modern bioinformatic scales are amazingly accurate, with a high level of internal consistency, and little ambiguity.
It is widely believed that cancerous mutations are formed initially as a few isolated cells with mutated DNA whose growth into tumors is controlled by autoantibodies. There are around ten million known cancer mutations, in principle controlled by a similar number (or more) of possible antibodies.
Monoclonal antibodies with anti-cancer and antiinflammatory functions are expensive, and recently this has turned attention to simplifying antibody-antigen interactions by focusing on selected antigenic proteins instead. Small antigenic regions (epitopes) interact with small antibody regions (paratopes). Small peptide epitope sequences are printed cost-effectively on microarrays.
Due to their miniature format they allow for the multiplex analysis of several thousands of peptides at the same time while requiring a minimal sample volume [1].
We have identified epitopic features in scans of several well studied proteins to universal hydropathic properties, as quantified very accurately by a modern thermodynamic scale Ψ.
Protein globular shapes are determined by competing hydrophobic forces (pushing phobic segments towards the globular cores) and hydrophilic forces (pushing philic segments towards the globular water interface). Moreover, the leading physicochemical properties determining protein aggregative mutations are hydrophobicity, secondary structure propensity and charge [2]. To quantify these effects in the classic period of molecular biology (before 2000), no less than 127 hydropathicity scales were proposed, but seldom compared for accuracy and applicability [3].
The modern hydropathicity scale Ψ, built by Brazilian bioinformaticists Moret and Zebende (MZ), is an interdisciplinary bridge connecting proteins to statistical mechanics and phase diagram critical points [4]. They evaluated solvent-exposed surface areas (SASA) of amino acids in > 5000 high-resolution (< 2A) protein segments, and fixed their attention on the central amino acid in each segment. The lengths of the small segments L = 2N + 1 varied from 3 to 45, but the interesting range turned out to be 9 ≤ L ≤ 35. Across this range they found linear behavior on a log-log plot for each of the 20 amino acids (aa):
Here Ψ(aa) is recognizable as a Mandelbrot fractal, suitable for quantifying second-order conformational changes [5]. It arises because the longer segments self-similarly fold back on themselves, occluding the SASA of the central aa. The most surprising aspect of this folded occlusion is that its self-similarity is nearly universal on average, and almost independent of the individual protein fold.
The sculpting effects of billions of years of protein aqueous evolution have smoothed globular differential geometries, as described by ( 1) for all proteins. For a specific protein self-similar smoothing effects also occur, but now on a modular wave length determined by the protein’s function. Given Ψ (aa), we calculate the modular average
which is a rectangular window of width W = 2M + 1. We will look at minimal values of W for protein strains used in the HPV vaccine.
Before we do so, one more concept is needed. Level sets were developed to track the motions of continuum interfaces [6] applied here to protein globular surfaces. Practical applications of level sets have emphasized image analysis [7], and have gradually evolved to include Voronoi partitioning, just as has been used for deriving protein hydropathicity scales since 1978 [8]. We expect, of course, that hydrophobic pivots move most slowly, while hydrophilic hinges move fastest. When there are two or more degenerate (level) pivots or hinges, it is likely that this is not accidental (nothing in proteins is), and we can test this assumption by comparing profiles with different scales. Synchronized motions should enable self-assembly [9].
The long road that led to cervical cancer vaccines began in 1976 when Harald zur Hausen published the Nobel hypothesis that human papilloma virus (HPV) plays an important role in the cause of cervical cancer. HPV is a large capsid protein, but it was found that only the L1 part was needed to make a good vaccine that conformationally self-assembled into morphologically correct virus-like particles (VLPs) [10]. L1 from HPV 16, taken from lesions that had not progressed to cancer, self-assembled 10 3 times more efficiently than the HPV 16 L1P that researchers everywhere had been using; the old strain L1P had been isolated from a cancer, which differed from L1 by only a single amino acid mutation D202H [11]. Vaccines based on the unaffected strain L1 are also surprisingly effective even for many strains mutated at sites S = {76,176,181,191,282,353,389,474}, singly or in combinations of up to 6 mutations [12]. The dramatic effect of the single amino acid mutation D202H on HPV vaccine effectiveness is a long-standing mystery, which profiling with fractal Ψ (aa,W) appears to solve. while the broad peak appears to be very stable, in accord with the genera
This content is AI-processed based on open access ArXiv data.
