Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions

Predicting cancer stages from tissue energy dissipation

Understanding cancer staging in order to predict its progression is vital to determine its severity and to plan the most appropriate therapies. This task has attracted interest from different fields of science and engineering. We propose a computational model that predicts the evolution of cancer in terms of the intimate structure of the tissue, considering that this is a self-organised structure that undergoes transformations governed by non-equilibrium thermodynamics laws. Based on experimental data on the dependence of tissue configurations on their elasticity and porosity, we relate the cancerous tissue stages with the energy dissipated, showing quantitatively that tissues in more advanced stages dissipate more energy. The knowledge of this energy allows us to know the probability of observing the tissue in its different stages and the probability of transition from one stage to another. We validate our results with experimental data and statistics from the World Health Organisation. Our quantitative approach provides insights into the evolution of cancer through its different stages, important as a starting point for new and integrative research to defeat cancer.

Therapeutic Potential of B12N12-X (X = Au, Os, and Pt) Nanostructured as Effective Fluorouracil (5Fu) Drug Delivery Materials

In view of the research-substantiated comparative efficiency of nontoxic and bioavailable nanomaterials synergic with human systems for drug delivery, this work was aimed at studying the comparative efficiency of transition metal (Au, Os, and Pt)-decorated B₁₂N₁₂ nanocages in the adsorption of fluorouracil (5Fu), an antimetabolite-classed anticarcinogen administered for cancers of the breast, colon, rectum, and cervix. Three different metal-decorated nanocages interacted with 5Fu drug at the oxygen (O) and fluorine (F) sites, resulting in six adsorbent-adsorbate systems whose reactivity and sensitivity were investigated using density functional theory computation at the B3LYP/def2TZVP level of theory with special emphasis on the structural geometry, electronic, and topology analysis as well as the thermodynamic properties of the systems. While the electronic studies predicted Os@F as having the lowest and most favorable E_gp and E_ad of 1.3306 eV and -11.9 kcal/mol, respectively, the thermodynamic evaluation showed Pt@F to have the most favorable thermal energy (E), heat capacity (C_p), and entropy (ΔS) values as well as negative ΔH and ΔG while the adsorption studies showed that the greatest degree of chemisorption with E_ad magnitude of -204.5023 kcal/mol was observed in energies ranging from -12.0 to 138.4 kcal/mol with Os@F and Au@F at the lower and upper borders. The quantum theory of atoms in molecules results show that the six systems had noncovalent interactions as well as a certain degree of partial covalency but none showed covalent interaction while the noncovalent interaction analysis corroborated this by showing that the six systems had favorable interactions, though of varying degrees, with very little trace of steric hindrance or electrostatic interactions. Overall, the study showed that notwithstanding the good performance of the six adsorbent systems considered, the Pt@F and Os@F showed the most favorable potential for the delivery of 5Fu.

An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations

Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity

PURPOSE

Neuroblastoma is a pediatric solid tumor with a prognosis associated with histology and age of the patient, which are the parameters of the well-established current classification (Shimada classification). Despite the development of new treatment options, the prognosis of high-risk neuroblastoma patients is still poor. Therefore, there is a continuous need to stratify the children suffering from this tumor. A mathematical and computational approach is proposed to enable automatic and precise cancer diagnosis on the histological slide.

METHODS

We targeted the complexity of neuroblastoma by calculating its image entropy (S), fractal dimension (FD), and lacunarity (λ) in a combined mathematical code. First, we tested the proposed method for patient-derived glioma images. It allowed distinguishing between normal brain tissue, grade II, and grade III glioma, which harbor different outcomes.

RESULTS

In neuroblastoma, our analysis of image's FD, S, and λ combined with a machine learning algorithm automatically predicted tumor malignancy with a receiver operating characteristic curve of 0.82. FD, S, and λ distinguish between neuroblastoma and ganglioneuroma, but they only partially differentiate between the normal samples and the other classes. Ganglioneuroma, the most differentiated form, and poorly-differentiated neuroblastoma display different values of FD, S, and λ.

CONCLUSIONS

FD, S, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children's hospitals worldwide.

A lesson for the maestro of the replication fork: Targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy

The proliferating cell nuclear antigen (PCNA) has emerged as a promising candidate for the development of novel cancer therapeutics. PCNA is a nononcogenic mediator of DNA replication that regulates a diverse range of cellular functions and pathways through a comprehensive list of protein-protein interactions. The hydrophobic binding pocket on PCNA offers an opportunity for the development of inhibitors to target various types of cancers and modulate protein-protein interactions. In the present study, we explored the binding modes and affinity of molecule I1 (standard molecule) with the previously suggested dimer interface pocket and the hydrophobic pocket present on the frontal side of the PCNA monomer. We also identified potential lead molecules from the library of in-house synthesized 3-methylenisoindolin-1-one based molecules to inhibit the protein-protein interactions of PCNA. Our results were based on robust computational methods, including molecular docking, conventional, steered, and umbrella sampling molecular dynamics simulations. Our results suggested that the standard inhibitor I1 interacts with the hydrophobic pocket of PCNA with a higher affinity than the previously suggested binding site. Also, the proposed molecules showed better or comparable binding free energies as calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach and further validated by enhanced umbrella sampling simulations. In vitro and in vivo methods could test the computationally suggested molecules for advancement in the drug discovery pipeline.

Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma

MicroRNA (miRNA)-based therapies are an emerging class of targeted therapeutics with many potential applications. Ewing Sarcoma patients could benefit dramatically from personalized miRNA therapy due to inter-patient heterogeneity and a lack of druggable (to this point) targets. However, because of the broad effects miRNAs may have on different cells and tissues, trials of miRNA therapies have struggled due to severe toxicity and unanticipated immune response. In order to overcome this hurdle, a network science-based approach is well-equipped to evaluate and identify miRNA candidates and combinations of candidates for the repression of key oncogenic targets while avoiding repression of essential housekeeping genes. We first characterized 6 Ewing sarcoma cell lines using mRNA sequencing. We then estimated a measure of tumor state, which we term network potential, based on both the mRNA gene expression and the underlying protein-protein interaction network in the tumor. Next, we ranked mRNA targets based on their contribution to network potential. We then identified miRNAs and combinations of miRNAs that preferentially act to repress mRNA targets with the greatest influence on network potential. Our analysis identified TRIM25, APP, ELAV1, RNF4, and HNRNPL as ideal mRNA targets for Ewing sarcoma therapy. Using predicted miRNA-mRNA target mappings, we identified miR-3613-3p, let-7a-3p, miR-300, miR-424-5p, and let-7b-3p as candidate optimal miRNAs for preferential repression of these targets. Ultimately, our work, as exemplified in the case of Ewing sarcoma, describes a novel pipeline by which personalized miRNA cocktails can be designed to maximally perturb gene networks contributing to cancer progression.

Application of Thermodynamics and Protein–Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy

In this paper, we propose a bioinformatics-based method, which introduces thermodynamic measures and topological characteristics aimed to identify potential drug targets for pharmaco-resistant epileptic patients. We apply the Gibbs homology analysis to the protein–protein interaction network characteristic of temporal lobe epilepsy. With the identification of key proteins involved in the disease, particularly a number of ribosomal proteins, an assessment of their inhibitors is the next logical step. The results of our work offer a direction for future development of prospective therapeutic solutions for epilepsy patients, especially those who are not responding to the current standard of care.

Information Rates of Controlled Protein Interactions Using Terahertz Communication

In this work, we present a paradigm bridging electromagnetic (EM) and molecular communication through a stimuli-responsive intra-body model. It has been established that protein molecules, which play a key role in governing cell behavior, can be selectively stimulated using Terahertz (THz) band frequencies. By triggering protein vibrational modes using THz waves, we induce changes in protein conformation, resulting in the activation of a controlled cascade of biochemical and biomechanical events. To analyze such an interaction, we formulate a communication system composed of a nanoantenna transmitter and a protein receiver. We adopt a Markov chain model to account for protein stochasticity with transition rates governed by the nanoantenna force. Both two-state and multi-state protein models are presented to depict different biological configurations. Closed form expressions for the mutual information of each scenario is derived and maximized to find the capacity between the input nanoantenna force and the protein state. The results we obtain indicate that controlled protein signaling provides a communication platform for information transmission between the nanoantenna and the protein with a clear physical significance. The analysis reported in this work should further research into the EM-based control of protein networks.

Biophysical signal transduction in cancer cells: Understanding its role in cancer pathogenesis and treatment

Signaling between cells can promote both the development and progression of cancer. It has been found that chemical and physical signals, together with extracellular factors, can influence cancer progression. In this review, we focus on the physical microenvironment of cancer cells and examine the action of mechanical, electromagnetic, thermal, and acoustic signals on cancer cells, which may provide new directions for cancer research and treatment.

Gibbs Free Energy Calculation of Mutation in PncA and RpsA Associated With Pyrazinamide Resistance

A central approach for better understanding the forces involved in maintaining protein structures is to investigate the protein folding and thermodynamic properties. The effect of the folding process is often disturbed in mutated states. To explore the dynamic properties behind mutations, molecular dynamic (MD) simulations have been widely performed, especially in unveiling the mechanism of drug failure behind mutation. When comparing wild type (WT) and mutants (MTs), the structural changes along with solvation free energy (SFE), and Gibbs free energy (GFE) are calculated after the MD simulation, to measure the effect of mutations on protein structure. Pyrazinamide (PZA) is one of the first-line drugs, effective against latent Mycobacterium tuberculosis isolates, affecting the global TB control program 2030. Resistance to this drug emerges due to mutations in pncA and rpsA genes, encoding pyrazinamidase (PZase) and ribosomal protein S1 (RpsA) respectively. The question of how the GFE may be a measure of PZase and RpsA stabilities, has been addressed in the current review. The GFE and SFE of MTs have been compared with WT, which were already found to be PZA-resistant. WT structures attained a more stable state in comparison with MTs. The physiological effect of a mutation in PZase and RpsA may be due to the difference in energies. This difference between WT and MTs, depicted through GFE plots, might be useful in predicting the stability and PZA-resistance behind mutation. This study provides useful information for better management of drug resistance, to control the global TB problem.

Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks

We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting.

Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age

We propose to use a Gibbs free energy function as a measure of the human brain development. We adopt this approach to the development of the human brain over the human lifespan: from a prenatal stage to advanced age. We used proteomic expression data with the Gibbs free energy to quantify human brain’s protein–protein interaction networks. The data, obtained from BioGRID, comprised tissue samples from the 16 main brain areas, at different ages, of 57 post-mortem human brains. We found a consistent functional dependence of the Gibbs free energies on age for most of the areas and both sexes. A significant upward trend in the Gibbs function was found during the fetal stages, which is followed by a sharp drop at birth with a subsequent period of relative stability and a final upward trend toward advanced age. We interpret these data in terms of structure formation followed by its stabilization and eventual deterioration. Furthermore, gender data analysis has uncovered the existence of functional differences, showing male Gibbs function values lower than female at prenatal and neonatal ages, which become higher at ages 8 to 40 and finally converging at late adulthood with the corresponding female Gibbs functions.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

Mutation in Cytochrome B gene causes debility and adverse effects on health of sheep

Cytochrome B is the mitochondrial protein, which functions as part of the electron transport chain and is the main subunit of transmembrane cytochrome bc1 and b6f complexes affecting energy metabolism through oxidative phosphorylation. The present study was conducted to study the effect of mutation of Cytochrome B gene on the health condition of sheep, which the first report of association of mitochondrial gene with disease traits in livestock species. Non-synonymous substitutions (F33 L and D171N) and Indel mutations were observed for Cytochrome B gene, leading to a truncated protein, where anemia, malfunctioning of most of the vital organs as liver, kidney and mineral status was observed and debility with exercise intolerance and cardiomyopathy in extreme cases were depicted. These findings were confirmed by bioinformatics analysis, haematological and biochemical data analysis, and other phenotypical physiological data pertaining to different vital organs. The molecular mechanism of cytochrome B mutation was that the mutant variant interferes with the site of heme binding (iron containing) domain and calcium binding essential for electron transport chain. Mutation at amino acid site 33 is located within transmembrane helix A, a hydrophobic environment at the Qi site and close to heme binding domain, and mutation effects these domain and diseases occur. Thermodynamic stability was also observed to decrease in mutant variant. Sheep Cytochrome B being genetically more similar to the human, it may be used as a model for studying human diseases related to cytochrome B defects. Future prospect of the study includes the therapeutic application of recombinant protein, gene therapy and marker-assisted selection of disease-resistant livestock.

Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem

Symmetry and symmetry breaking concepts from physics and biology are applied to the problem of cancer. Three categories of symmetry breaking in cancer are examined: combinatorial, geometric, and functional. Within these categories, symmetry breaking is examined for relevant cancer features, including epithelial-mesenchymal transition (EMT); tumor heterogeneity; tensegrity; fractal geometric and information structure; functional interaction networks; and network stabilizability and attack tolerance. The new cancer symmetry concepts are relevant to homeostasis loss in cancer and to its origin, spread, treatment and resistance. Symmetry and symmetry breaking could provide a new way of thinking and a pathway to a solution of the cancer problem.

Personalized anticancer therapy selection using molecular landscape topology and thermodynamics

Personalized anticancer therapy requires continuous consolidation of emerging bioinformatics data into meaningful and accurate information streams. The use of novel mathematical and physical approaches, namely topology and thermodynamics can enable merging differing data types for improved accuracy in selecting therapeutic targets. We describe a method that uses chemical thermodynamics and two topology measures to link RNA-seq data from individual patients with academically curated protein-protein interaction networks to select clinically relevant targets for treatment of low-grade glioma (LGG). We show that while these three histologically distinct tumor types (astrocytoma, oligoastrocytoma, and oligodendroglioma) may share potential therapeutic targets, the majority of patients would benefit from more individualized therapies. The method involves computing Gibbs free energy of the protein-protein interaction network and applying a topological filtration on the energy landscape to produce a subnetwork known as persistent homology. We then determine the most likely best target for therapeutic intervention using a topological measure of the network known as Betti number. We describe the algorithm and discuss its application to several patients.

Gibbs free energy as a measure of complexity correlates with time within C. elegans embryonic development

We investigate free energy behavior in the nematode Caenorhabditis elegans during embryonic development. Our approach utilizes publicly available gene expression data, which gives us a picture of developmental changes in protein concentration and, resultantly, chemical potential and free energy. Our results indicate a clear global relationship between Gibbs free energy and time spent in development and provide thermodynamic indicators of the large-scale biological events of cell division and differentiation.

Proteomic indicators of oxidation and hydration state in colorectal cancer

New integrative approaches are needed to harness the potential of rapidly growing datasets of protein expression and microbial community composition in colorectal cancer. Chemical and thermodynamic models offer theoretical tools to describe populations of biomacromolecules and their relative potential for formation in different microenvironmental conditions. The average oxidation state of carbon (Z_C) can be calculated as an elemental ratio from the chemical formulas of proteins, and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O) is computed by writing the overall formation reactions of proteins from basis species. Using results reported in proteomic studies of clinical samples, many datasets exhibit higher mean Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of proteins in carcinoma or adenoma compared to normal tissue. In contrast, average protein compositions in bacterial genomes often have lower Z_C for bacteria enriched in fecal samples from cancer patients compared to healthy donors. In thermodynamic calculations, the potential for formation of the cancer-related proteins is energetically favored by changes in the chemical activity of H₂O and fugacity of O₂ that reflect the compositional differences. The compositional analysis suggests that a systematic change in chemical composition is an essential feature of cancer proteomes, and the thermodynamic descriptions show that the observed proteomic transformations in host tissue could be promoted by relatively high microenvironmental oxidation and hydration states.