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J. C. Phillips

Publications and source records attributed to J. C. Phillips.

At least 37 records · Page 2Linked to original sources

Self-Organized Networks with Long-Range Interactions: Tandem Darwinian Evolution of $α$ and $β$ Tubulin

Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). Our theoretical analysis uses hydropathic waves to identify and contrast the functional differences between the polymerizing $α$ and $β$ tubulin monomers, which are similar in length and secondary structures, as well as having indistinguishable phylogenetic trees. We show how evolution has improved water-driven flexibility especially for $α$ tubulin, and thus facilitated heterodimer microtubule assembly, in agreement with recent atomistic simulations and topological models. We conclude that the failure of phylogenetic analysis to identify functionally specific positive Darwinian evolution has been caused by 20th century technical limitations. These are overcome using 21st century quantitative mathematical methods based on thermodynamic scaling and hydropathic modular averaging. Our most surprising result is the identification of large level sets, especially in hydrophobic extrema, with both thermodynamically first- and second-order scaled water waves. Our calculations include explicitly long-range water-protein interactions described by fractals. We also suggest a much-needed corrective for large protein drug development costs.

q-bio.OT↗

Synchronized Pivots

Thermodynamic profiles, based on bioinformatic hydropathicity scales and membrane-fitted sliding windows, exhibit accurately level sets of hydrophobic peaks or valleys in the constant fragment Fc of monoclonal antibodies. These sets provide a roadmap for selected short regions where a few directed amino acid mutations could produce either accelerated antibody kinetics or increased stability. These profiles may reveal dramatic differences in antibody activity

q-bio.BM↗

Self-Organized Networks, Darwinian Evolution of Self-Organized Networks, Darwinian Evolution of Dynein Rings, Stalks and Stalk Heads

Cytoskeletons are self organized networks based on polymerized proteins, actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality self organized criticality. Dynein has three distinct titled subunits, but how these units connect to function as a molecular motor is mysterious. Dynein binds to tubulin through two coiled coil stalks and a stalk head. The energy used to alter the head binding and propel cargo along tubulin is supplied by ATP at a ring 1500 amino acids away.

q-bio.MN↗

Why Abeta42 Is Much More Toxic Than Abeta40

Amyloid precursor with 770 amino acids dimerizes and aggregates, as do its c terminal 99 amino acids and amyloid 40,42 amino acids fragments. The titled question has been discussed extensively, and here it is addressed further using thermodynamic scaling theory to analyze mutational trends in structural factors and kinetics. Special attention is given to Family Alzheimer's Disease mutations outside amyloid 42. The scaling analysis is connected to extensive docking simulations which included membranes, thereby confirming their results and extending them to Amyloid precursor.

q-bio.OT↗

Evolution of the Ubiquitin-Activating Enzyme Uba1 (E1)

Ubiquitin tags diseased proteins and initiates an enzyme conjugation cascade, which has three stages. The first-stage enzyme Uba1 (E1) has evolved only modestly from slime mold to humans, and is > 14 times larger than Ub. Here we use critical point thermodynamic scaling theory to connect Uba1 (E1) evolution from yeast and slime mold to fruit flies and humans to subtle changes in its amino acid sequences.

q-bio.OT↗

Configuration Interaction of Hydropathic Waves Enables Ubiquitin Functionality

Ubiquitin, discovered less than 50 years ago, tags thousands of diseased proteins for destruction. It is small (only 76 amino acids), and is found unchanged in mammals, birds, fish and even worms. Key features of its functionality are identified here using critical point thermodynamic scaling theory. These include Fano interference between first- and second-order elements of globular surface shape transitions. Comparison with its closest relative, 76 amino acid Nedd8, shows that the latter lacks these features. A cracked elastic network model is proposed for the common target shared by many diseased proteins.

q-bio.OT↗

Giant Hub Src and Syk Tyrosine Kinase Thermodynamic Profiles Recapitulate Evolution

Thermodynamic scaling theory, previously applied mainly to small proteins, here analyzes quantitative evolution of the titled functional network giant hub enzymes. The broad domain structure identified homologically is confirmed hydropathically using amino acid sequences only. The most surprising results concern the evolution of the tyrosine kinase globular surface roughness from avian to mammals, which is first order, compared to the evolution within mammals from rodents to humans, which is second order. The mystery of the unique amide terminal region of proto oncogene tyrosine protein kinase is resolved by the discovery there of a septad targeting cluster, which is paralleled by an octad catalytic cluster in tyrosine kinase in humans and a few other species. These results, which go far towards explaining why these proteins are among the largest giant hubs in protein interaction networks, use no adjustable parameters.

q-bio.OT↗

Quantitative Molecular Scaling Theory of Protein Amino Acid Sequences, Structure, and Functionality

Here we review the development of protein scaling theory, starting from backgrounds in mathematics and statistical mechanics, and leading to biomedical applications. Evolution has organized each protein family in different ways, but scaling theory is both simple and effective in providing readily transferable dynamical insights complementary for many proteins represented in the 90 thousand static structures contained in the online Protein Data Base (PDB). Scaling theory is a simplifying magic wand that enables one to search the hundreds of millions of protein articles in the Web of Science, and identify those proteins that present new cost-effective methods for early detection and/or treatment of disease through individual protein sequences (personalized medicine). Critical point theory is general, and recently it has proved to be the most effective way of describing protein networks that have evolved towards nearly perfect functionality in given environments (self-organized criticality). Evolutionary patterns are governed by common scaling principles, which can be quantified using scales that have been developed bioinformatically by studying thousands of PDB structures. The most effective scales involve either hydropathic globular sculpting interactions averaged over length scales centered on membrane dimensions, or exposed beta strand propensities associated with aggregative (strong) protein-protein interactions.

q-bio.OT↗

Mechanism for long-acting chimeras based on fusion with the carboxyl-terminal peptide (CTP) of human chorionic gonadotropin beta-subunit 3

Thermodynamic scaling explains the dramatic successes of CTP fused human growth proteins as regards lifetime in vivo and enhanced functionality compared to their wild-type analogues, like Biogen. The theory is semi-quantitative and contains no adjustable parameters. It shows how hydrophilic terminal spheres orient fused proteins in the neighborhood of a membrane surface, extending lifetimes and improving functionality.

q-bio.QM↗

Minimal Immunogenic Epitopes Have Nine Amino Acids

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.

q-bio.OT↗

Autoantibody recognition mechanisms of MUC1

The most cost-effective blood-based, noninvasive molecular cancer biomarkers are based on p53 epitopes and MUC1 tandem repeats. Here we use dimensionally compressed bioinformatic fractal scaling analysis to compare the two distinct and comparable probes, which examine different sections of the autoantibody population, achieving combined sensitivities of order 50%. We discover a promising MUC1 epitope in the SEA region outside the tandem repeats.

q-bio.OT↗

Hemoglobin Strain Field Waves and Allometric Functionality

Hemoglobin (Hgb) forms tetramers (dimerized dimers), which enhance its globular stability and may also facilitate small gas molecule transport, as shown by recent all-atom Newtonian solvated simulations. Hydropathic bioinformatic scaling reveals many wave-like features of strained Hgb structures at the coarse-grained amino acid level, while distinguishing between these features thermodynamically. Strain fields localized near hemes interfere with extended strain fields associated with dimer interfacial misfit, resulting in wave-length dependent dimer correlation function antiresonances.

q-bio.BM↗

Hidden Thermodynamic Information in Protein Amino Acid Mutation Tables

We combine the standard 1992 20x20 substitution matrix based on block alignment, BLOSUM62, with the standard 1982 amino acid hydropathicity scale KD as well as the modern 2007 hydropathicity scale MZ, and compare the results. The 20-parameter KD and MZ hydropathicity scales have different thermodynamic character, corresponding to first and second order transitions. The KD and MZ comparisons show that the mutation rates reflect quantitative iteration of qualitative amino acid phobic and philic binary 2x10 properties that define quaternary 4x5 subgroups (but not quinary 5x4 subgroups), with the modern MZ bioinformatic scale giving much better results. The quaternary 5 mer MZ 4x5 subgroups are called mutons (Mu5).

q-bio.OT↗

Early Recognition of Emerging Flu Strain Clusters

Minimizing time delays in manufacturing vaccines appropriate to rapidly mutating viruses is the key step for improving vaccine effectiveness. The vaccine for the H3N2 flu type has failed for the last two years (~ 15% effective). Here we summarize the state of the predictive art and report the most current results for H3N2 flu vaccine design. Using a 2006 model of dimensional reduction of viral mutational complexity, we show that this model can reduce vaccine time delays by a year or more in some cases.

q-bio.PE↗

Bioinformatic Scaling of Allosteric Interactions in Biomedical Isozymes

Allosteric (long-range) interactions can be surprisingly strong in proteins of biomedical interest. Here we use bioinformatic scaling to connect prior results on nonsteroidal anti-inflammatory drugs to promising new drugs that inhibit cancer cell metabolism. Many parallel features are apparent, which explain how even one amino acid mutation, remote from active sites, can alter medical results. The enzyme twins involved are cyclooxygenase (aspirin) and isocitrate dehydrogenase (IDH). The IDH results are accurate to 1% and are overdetermined by adjusting a single bioinformatic scaling parameter. It appears that the final stage in optimizing protein functionality may involve leveling of the hydrophobic cutoffs of the arms of conformational hydrophilic hinges.

q-bio.QM↗

Autoantibody recognition mechanisms of p53 epitopes

There is an urgent need for economical blood based, noninvasive molecular biomarkers to assist in the detection and diagnosis of cancers in a cost effective manner at an early stage, when curative interventions are still possible. Serum autoantibodies are attractive biomarkers for early cancer detection, but their development has been hindered by the punctuated genetic nature of the ten million known cancer mutations. A recent study of 50,000 patients (Pedersen et al., 2013) showed p53 15mer epitopes are much more sensitive colon cancer biomarkers than p53, which in turn is a more sensitive cancer biomarker than any other protein. The function of p53 as a nearly universal tumor suppressor is well established, because of its strong immunogenicity in terms of not only antibody recruitment, but also stimulation of autoantibodies. Here we examine bioinformatic fractal scaling analysis for identifying sensitive epitopes from the p53 amino acid sequence, and show how it could be used for early cancer detection (ECD). We trim 15mers to 7mers, and identify specific 7mers from other species that could be more sensitive to aggressive human cancers, such as liver cancer.

q-bio.QM↗

Vaccine escape in 2013-4 and the hydropathic evolution of glycoproteins of A/H3N2 viruses

More virulent strains of influenza virus subtypes H1N1 appeared widely in 2007 and H3N2 in 2011, and especially 2013-4, when the effectiveness of the H3N2 vaccine decreased nearly to zero. The amino acid differences of neuraminidase from prior less virulent strains appear to be small (<1%) when tabulated through sequence alignments and counting site identities and similarities. Here we show how analyzing fractal hydropathic forces responsible for neuraminidase globular compaction and modularity quantifies the mutational origins of increased virulence. It also predicts vaccine escape and specifies optimized targets for the 2015 H3N2 vaccine different from the WHO target. Unlike some earlier methods based on measuring hemagglutinin antigenic drift and ferret sera, which take several years, cover only a few candidate strains, and are ambiguous, the new methods are timely and can be completed, using NCBI and GISAID amino acid sequences only, in a few days.

q-bio.OT↗

Stress partition and micro-structure in size-segregating granular flows

When a granular mixture involving grains of different sizes is shaken, sheared, mixed, or left to flow, grains tend to separate by sizes in a process known as size segregation. In this study, we explore the size segregation mechanism in granular chute flows in terms of pressure distribution and granular micro-structure. Therefore, 2D discrete numerical simulations of bi-disperse granular chute flows are systematically analysed. Based on the theoretical models by Gray and Thornton 2005 and Hill and Tan 2014, we explore the stress partition in the phases of small and large grains, discriminating between contact stresses and kinetic stresses. Our results support both gravity-induced and shear-gradient-induced segregation mechanisms. However, we show that the contact stress partition is extremely sensitive to the definition of the partial stress tensors, and more specifically, to the way mixed contacts (i.e. involving a small grain and a large grain) are handled, making conclusions on gravity-induced segregation uncertain. By contrast, the computation of the partial kinetic stress tensors is robust. Kinetic pressure partition exhibits a deviation from continuum mixture theory of a significantly larger amplitude than contact pressure, and display a clear dependence on the flow dynamics. Finally, using a simple approximation for the contact partial stress tensors, we investigate how contact stress partition relates with the flow micro-structure, and suggest that the latter may provide an interesting proxy for studying gravity-induced segregation.

cond-mat.soft↗