Computational Studies

This work deals with first-principles and in silico studies of graphene oxide-based whole-cell selective aptamers for cancer diagnostics utilising a tunable-surface strategy. Herein, graphene oxide (GO) was constructed as a surface-based model with poly(N-isopropylacrylamide) (PNIPAM) covalently grafted as an "on/off"-switch in triggering interactions with the cancer-cell protein around its lower critical solution temperature. The atomic building blocks of the aptamer and the PNIPAM adsorbed onto the GO was investigated at the density functional theory (DFT) level. The presence of the monomer of PNIPAM stabilised the system's π-π interaction between GO and its nucleobases as confirmed by higher bandgap energy, satisfying the eigenvalues of the single-point energy observed rather than the nucleobase and the GO complex independently. The unaltered geometrical structures of the surface emphasise the physisorption type interaction between the nucleobase and the GO/NIPAM surface. The docking result for the aptamer and the protein, highlighted the behavior of the PNIPAM-graft-GO is exhibiting globular and extended conformations, further supported by molecular dynamics (MD) simulations. These studies enabled a better understanding of the thermal responsive behavior of the polymer-enhanced GO complex for whole-cell protein interactions through computational methods. Carbon-based structures are unique materials widely used in various fields due to excellent thermal, mechanical, and electrical properties. Moreover, the interactions with biomolecules harness the benefits in numerous applications , particularly in the field of biosensors, medical diagnostics and drug delivery systems 1-5. Its applications regarding graphene and graphene oxide are expected to be less toxic at low concentrations. Hence, they are readily applicable to biomedical functionalities. Graphene oxide (GO) is an exfoliated single-layer graphene sheet with oxygen functional groups distributed along its surface. It has gained widespread recognition due to its excellent conductivity and feasibility for microfabrication, large surface area, high optical transparency, and surface bio-compatibility in biomedical applications 6-9. These properties are attributed to its unique contents which consist of sp 2 hybridised carbon and sp 3 hybridised carbon and oxygen functional groups surrounding the surface. GO, is widely associated with DNA through its non-covalent interactions, thereby generating a complex intercalated structure 10,11 .

Quantum  calculations  of  the  physical  properties  (electronic  and  vibrational),  based  on  density  functional  theory  (DFT) method at B3LYP/6-31G** level of theory, were performed, by means of the Gaussian 09 set of programs, to investigate the effect of the addition of the radical CN on pyridazine molecules. The best geometry, the total energy, frontier molecular orbital energies  (HOMO and HUMO),  energy  gap,  ionization  potential,  electron  affinity,  electronegativity,  chemical  hardness,  chemical  softness,  electrophilicity, dipole moment and the harmonic vibration frequencies were calculated and discussed for the study molecules. The electronic properties are computed by two different methods, a finite difference approximation and Koopman’s theorem. The study clearly shows that adding the radicals CN cause decreased the energy gap and the chemical hardness, and increase the electrophilicity. Therefore, the p resence of these  radicals  improves  the  conductivities  and  enhances  the  solubility  and  reactivity.  All  the  results  indicate  that  the  molecule 2,3-(CH) ₄N₂ (CN)2 is the best option for n-type organic semiconductors.

Unbalanced uptake of Omega 6/Omega 3 (o-6/o-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products (e.g., meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables (V k) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of o-6/o-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions (c j) like c 1 = acid and/or base methylation treatments, c 2 = with/without fermentation, c 3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters (V k), the corresponding Box–Jenkins (DV kj) and PT operators (DDV kj) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy 4 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erd + os–Ré nyi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.