Journal of Computational Biophysics and Chemistry

Persistent path homology represents an advanced mathematical approach explicitly tailored for directed systems. With its remarkable ability to characterize unbalanced or asymmetrical relationships within data, this method demonstrates great promise in qualitative analysis of the intrinsic topological features present in materials and molecules. In this work, we introduce persistent path homology at the first time for carborane analysis. Intrinsic path topological features are used to predict the stability of closo-carboranes. We qualitatively explain the connection between path topological features and properties on o-C₂B${}_{10}$H${}_{12}$, m-C₂B${}_{10}$H${}_{12}$ and p-C₂B${}_{10}$H${}_{12}$. The correlation coefficients between linear predictions based on persistent path homology and thermodynamic stability are higher than 0.95, and that for chemical stability are about 0.85. While the correlation coefficients based on nonlinear models are increased to 0.99 and 0.95, respectively. These results indicate that persistent path homology shows excellent capabilities in structural and stability analysis of multi-element cluster physics.

Cryptosporidium parvum (C. parvum) is an apicomplexan protozoan known as a frequent cause of diarrhea. Because of its severe outbreaks and the association with different types of gastrointestinal cancers, it became an urgent need to develop effective solutions against that parasite. In the current study, the complete proteome of C. parvum was analyzed, and after applying several filtration steps, two proteins, namely Glycoprotein 60 (gp40/15) and thrombospondin-related adhesive protein (TRAP C-1), were selected as the protein candidates for epitope prediction. Next, CTL, HTL and BCL epitopes of each protein candidate were defined, and the best epitopes of each category were assembled alongside suitable linkers and adjuvants to initiate a potential multitope vaccine construct finally. That construct was evaluated for several properties, including its antigenicity, allergenicity, stability, secondary and tertiary structure, and the ability of that 3D predicted model to bind to TLR4. Furthermore, the stability of the constructed vaccine ligand in the TLR4 receptor was deeply investigated through a molecular dynamics simulation. The current study describes the stages of the multitope construct design. It demonstrates the results of that construct computational evaluation where the generated scores support the nomination of that potential vaccine as a solution for C. parvum infection, and further wet lab experiments are required to validate our current findings.

Background: The emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, notably Omicron and Delta, necessitates a detailed analysis of the amino acid substitutions occurring within the spike (S) protein. The substitutions mentioned above are crucial in regulating the interactions between the virus and its host receptor angiotensin-converting enzyme 2 (ACE2), in addition to impacting the effectiveness of monoclonal antibodies (mAbs) used in therapy. Methods: In this paper, we employed computational mutational analysis using PremPS to predict the stability scores of the S protein. Our analysis highlighted several key mutations, including G181V and A222V, which exhibited significant alterations in stability, particularly within the N-terminal domain (NTD), and were notably prevalent in the Omicron variant. To further elucidate the impact of these mutations, we conducted docking simulations employing HADDOCK 2.4 to assess the binding affinity between the Omicron receptor-binding domain (RBD) and ACE2. Results: Our outcomes emerged that the Omicron RBD has a stronger attraction to ACE2, driven by mutations such as Q493R, Q498R, S477N, T478K, G496S and L452R, which significantly influenced the RBD-ACE2 complex dynamics. Additionally, we ascertained the potential for immune evasion by these mutations through docking simulations with mAbs. Our findings identified specific interactions between mutations like L452R, E484Q/A and Q493R with mAbs, suggesting their potential to evade immune recognition. Conclusions: In summary, our paper provides insight into the complex connection between the fluctuation of the SARS-CoV-2 S protein, receptor binding dynamics, and immunotherapeutic efficacy. These insights are essential for guiding the development of novel therapeutics and informing public health strategies aimed at combating the ongoing COVID-19 pandemic.

Ferritin stores iron as Fe${}^{3+}$ after being oxidized in the presence of molecular oxygen. The Fe${}^{2+}$ ions typically enter into the ferroxidase as hexahydrate, where the oxidation of Fe${}^{2+}$to Fe${}^{3+}$ occurs. The studies predict probable residues involved in the entry of molecular oxygen by sequence and structural comparison with other proteins. This study identified the probable $\pi$-H paths for electron transfer and proton translocation in ferritin through structural analysis. The identification of multiple proton translocation paths and the use of hexahydrate iron as substrate by ferritin indicate that during the oxidation of Fe${}^{2+}$ to Fe${}^{3+}$, all the water molecules of iron may be utilized. The resulting byproducts, protons and hydroxyl ions may be one of the sources for the proton gradient required to drive oxidative phosphorylation by electron transport complexes in mitochondria. Also, it may be required to acidify lysosomes and generate nitroperoxy species.

Human Prk kinase plays an important role in the maintenance of inflammatory stability and has been established as an attractive target of diverse rheumatic immune diseases. The kinase possesses a highly conserved polo-box domain (PBD) in its C-terminal noncatalytic region, which exhibits a wide specificity across its various substrates to regulate the kinase-mediated substrate phosphorylation. In this study, peptide quantitative structure-activity relationship (pQSAR) was systematically modeled based on a gene ontology-enriched set of Prk PBD-binding peptide data, which was structurally characterized using global descriptors and statistically correlated with peptide affinities via both linear and nonlinear machine learning regressions. The optimal pQSAR predictor was then employed to guide the genetic evolution (GE) of a random peptide group toward improved potency to Prk PBD domain, from which a number of hits were measured to have moderate or high affinity. The resulting Pm7 peptide was determined as a good binder that can effectively interact with the domain at sub-micromolar level, which was then used as a parent template to further derive other promising candidates by combining systematic residue mutagenesis and structure-based rescoring. Two Pm7-derived counterparts Pm72 and Pm74 were rationally designed to exhibit a satisfactory binding profile to Prk PBD domain, with affinity improved by 3.1-fold and 4.6-fold relative to their parent Pm7. Structural and energetic analyses revealed that the potent Prk PBD-binding sequences can be divided into a N-terminal polar section, a middle positively charged anchor and a C-terminal hydrophobic section, thus conferring both stability and specificity to the domain-peptide recognition and association.

Despite their use in ophthalmology for more than a century, there are no fundamental biophysical models that account for the dissolution rate of intra-ocular gas bubbles in vivo, as a function of their initial size and composition. Any physical model that reliably predicts the evolution of these bubbles would be extremely useful for planning treatment, by predicting their size over time, and their periods of clinical usefulness. A fundamental, computational, biophysical model is developed here with these purposes in mind. The model is fundamental in the sense that the underlying rate equations are derived using linear response theory, wherein the driving force is a solute chemical potential difference. It is computational because the rate equations for the bubble volume and composition are fully coupled and must be integrated numerically and simultaneously. It was rendered clinically relevant by fitting its two adjustable parameters (two rate constants) to recently acquired live human eye data on intra-ocular air bubble dissolution rates. The model consists of an explicitly four-component, dome-shaped gas bubble, comprised of N₂, O₂, H₂O and CO₂, which exchanges N₂ and O₂ with circulating venous blood in the capillaries of the eye. The calibrated model provided an excellent fit to the available data, and interpolated nicely through three points not used for the calibration. It was used to show that by selecting different N₂/O₂ ratios in the initially injected gas, one can generate intra-ocular gas bubbles with significantly different predicted periods of clinical usefulness. This potentially useful predictive capability does not exist in current clinical practice.

Over the past few years, more studies have been conducted on the use of photonic crystals (PCs) in the detection field. Particularly fascinating for use as gas sensors are these materials’ outstanding spectrum sensitivity and small design. Using one-dimensional PCs as the theoretical foundation, this work aims to develop and analyze a nanoscale system that can identify the concentration of gases in ambient air and differentiate it from contaminated air. The intended structure is composed of layers of gallium nitride (GaN) and aluminum nitride (AlN) that alternate. In between these layers is a defective layer cavity that is filled with polluted air, which has a refractive index that is quite similar to pure air. The transfer matrix method (TMM) was used to perform numerical results for the proposed sensor. This sensor achieves average sensitivity of 1791 nm/RIU, average quality factor and figure of merit (FoM) of 1833 and 2287, respectively. We therefore think that the suggested detector’s design offers a strong candidate for accurate and efficient detection.

Non-fullerene acceptor (NFA) molecules attracted huge attention from the photovoltaic community for their potential use in organic solar cells (OSCs). Herein, we designed a series of NFA materials (AR1–AR9) for OSCs by altering end-capped moieties. We employed various advanced density functional theory (DFT) and time-dependent (TD-DFT) approaches to characterize this designed AR1–AR9 series. Compared to the synthetic reference molecule ($R)$, designed molecules AR1–AR9 revealed a smaller bandgap (1.85 eV), and improved absorption spectra (λ_max 741 nm) in comparison to $R$ (λ_max 668 nm), indicating the better potential for the tailored molecules. Furthermore, the designed series (AR1–AR9) shows lower reorganization energy values (for holes and electrons) and lower binding energies (0.323 eV), whereas, improved charge mobilities, showing them better candidates for OSCs. Moreover, frontier molecular orbitals (FMO), open-circuit voltages (V_oc) and improved charge-transfer characteristics have also been investigated. These findings revealed that all developed NFAs exhibited a wide range of exciton dissociation constants and enhanced absorption efficiency with a relatively lower LUMO energy level. Therefore, we believe that these materials will be beneficial in boosting the power conversion efficiency (PCE) of OSC devices by improving their optoelectronic and photophysical properties.