Probing binding mechanism of interleukin-6 and olokizumab: in silico design of potential lead antibodies for autoimmune and inflammatory diseases

Computer-aided antibody engineering has been successful in the design of new biologics for disease diagnosis and therapeutic interventions. Interleukin-6 (IL-6), a well-recognized drug target for various autoimmune and inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, and psoriasis, was investigated in silico to design potential lead antibodies. Here, crystal structure of IL-6 along with monoclonal antibody olokizumab was explored to predict antigen–antibody (Ag???Ab)-interacting residues using DiscoTope, Paratome, and PyMOL. Tyr56, Tyr103 in heavy chain and Gly30, Ile31 in light chain of olokizumab were mutated with residues Ser, Thr, Tyr, Trp, and Phe. A set of 899 mutant macromolecules were designed, and binding affinity of these macromolecules to IL-6 was evaluated through Ag???Ab docking (ZDOCK, ClusPro, and Rosetta server), binding free-energy calculations using Molecular Mechanics/Poisson Boltzman Surface Area (MM/PBSA) method, and interaction energy estimation. In comparison to olokizumab, eight newly designed theoretical antibodies demonstrated better result in all assessments. Therefore, these newly designed macromolecules were proposed as potential lead antibodies to serve as a therapeutics option for IL-6-mediated diseases.     

Variable selection in nonlinear function-on-scalar regression

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003–2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.     

Nitric oxide blocks cellular heme insertion into a broad range of heme proteins

Although the insertion of heme into proteins enables their function in bioenergetics, metabolism, and signaling, the mechanisms and regulation of this process are not fully understood. We developed a means to study cellular heme insertion into apo-protein targets over a 3-h period and then investigated how nitric oxide (NO) released from a chemical donor (NOC-18) might influence heme (protoporphyrin IX) insertion into seven targets that present a range of protein structures, heme ligation states, and functions (three NO synthases, two cytochrome P450's, catalase, and hemoglobin). NO blocked cellular heme insertion into all seven apo-protein targets. The inhibition occurred at relatively low (nM/min) fluxes of NO, was reversible, and did not involve changes in intracellular heme levels, activation of guanylate cyclase, or inhibition of mitochondrial ATP production. These aspects and the range of protein targets suggest that NO can act as a global inhibitor of heme insertion, possibly by inhibiting a common step in the process.     

Tunable luminescence of a synthesized furophenanthraquinone derivative: interactions with different solvents

The synthesis is described of a luminescent furophenanthraquinone derivative, 9‐methoxyphenanthro[4,3‐b]furan‐4,5‐dione (MPFD). The biological importance of tetracyclic furophenanthraquinones was considered and the tunable luminescence of MPFD in different solvents was studied to explore the nature of the specific interactions between MPFD and solvents. Observation of dual emission bands and identical nature of the fluorescence excitation spectra of MPFD monitored at the emission wavelength in polar solvents indicated the formation of two different types of species in the excited state, probably due to proton transfer from the solvent to MPFD. Luminescence intensity due to anionic species was found to be increased and the corresponding peak was red shifted with increase in the proton‐donating ability of the solvents, acting as an acid with respect to MPFD. Availability of more acidic protons in the solvent facilitated this phenomenon occurring in the excited state. MPFD also interacted with halogen‐containing solvents by forming electron donor–acceptor charge transfer (CT) complexes. This CT complex formation was dependent on the number of chlorine atoms; the position of the corresponding luminescence band varied with the polarity of the solvent. Extent of the CT increased with increase in the number of chlorine atoms in the dichloro, trichloro and tetrachloro solvents, whereas the luminescence peak due to the CT complex was found to be blue shifted with decrease in solvent polarity. Interaction of the synthesized bioactive MPFD with different solvents deserves biological importance as proton transfer and CT play pivotal roles in biology.     

Synthesis of a Smart Gold Nano‐vehicle for Liver Specific Drug Delivery

Targeting drug formulations to specific tissues and releasing the bioactive content in response to a certain stimuli remains a significant challenge in the field of biomedical science. We have developed a nanovehicle that can be used to deliver “drugs” to “specific” tissues. For this, we have simultaneously modified the surface of the nanovehicle with “drugs” and “tissue-specific ligands”. The “tissue-specific ligands” will target the nanovehicle to the correct tissue and release the “drug” of interest in response to specific stimuli. We have synthesised a “lactose surface-modified gold nanovehicle” to target liver cells and release the model fluorescent drug (coumarin derivative) in response to the differential glutathione concentration (between blood plasma and liver cells). Lactose is used as the liver-specific targeting ligand given the abundance of l-galactose receptors in hepatic cells. The coumarin derivative is used as a fluorescent tag as well as a linker for the attachment of various biologically relevant molecules. The model delivery system is compatible with a host of different ligands and hence could be used to target other tissues as well in future. The synthesised nanovehicle was found to be non-toxic to cultured human cell lines even at elevated non-physiological concentrations as high as 100 μg/mL. We discover that the synthesised gold-based nanovehicle shows considerable stability at low extracellular glutathione concentrations; however coumarin is selectively released at high hepatic glutathione concentration.     

Smokeless Tobacco Extract (STE)-Induced Toxicity in Mammalian Cells is Mediated by the Disruption of Cellular Microtubule Network: A Key Mechanism of Cytotoxicity

Smokeless tobacco usage is a growing public health problem worldwide. The molecular mechanism(s) underlying smokeless tobacco associated tissue damage remain largely unidentified. In the present study we have tried to explore the effects of aqueous extract of smokeless tobacco (STE) on tubulin-microtubule, the major cytoskeleton protein that maintains cells morphology and participates in cell division. Exposure to STE resulted in dose-dependent cytotoxicity in a variety of mammalian transformed cell lines such as human lung epithelial cells A549, human liver epithelial cells HepG2, and mouse squamous epithelial cells HCC7, as well as non-tumorogenic human peripheral blood mononuclear cells PBMC. Cellular morphology of STE-treated cells was altered and the associated disruption of microtubule network indicates that STE targets tubulin-microtubule system in both cell lines. Furthermore it was also observed that STE-treatment resulted in the selective degradation of cellular tubulin, whereas actin remains unaltered. In vitro, polymerization of purified tubulin was inhibited by STE with the IC₅₀ value∼150 µg/ml and this is associated with the loss of reactive cysteine residues of tubulin. Application of thiol-based antioxidant N-acetyl cysteine (NAC) significantly abrogates STE-mediated microtubule damage and associated cytotoxicity in both A549 and HepG2 cells. These results suggest that microtubule damage is one of the key mechanisms of STE-induced cytotoxity in mammalian cells.     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.