Molecular Dynamics Simulations Approach for the Characterization of Peptides with Respect to Hydrophobicity

In vivo and computational investigation of butin against alloxan-induced diabetes via biochemical, histopathological, and molecular interactions

Herbs have been used as medicines since antiquity, and it has been discovered that the human body responds well to herbal remedies. Research on the effect of butin was conducted in the current study in the alloxan-induced diabetic rat paradigm. A total of 30 Wistar rats were randomly assigned into the following groups (n = 6): I-Normal; II-Alloxan-induced (50 mg/kg); III-Alloxan + butin 25 mg/kg; IV-Alloxan + butin 50 mg/kg; V-Butin per se 50 mg/kg. Various diabetic parameters (blood glucose, insulin, HbA1c), lipid profile, inflammatory (TNF-α, IL-1β, IL-6 and NF-κB), antioxidant enzymes (CAT, SOD and GSH), oxidative stress indicators (MDA), apoptosis marker (caspase-3), hepatic markers (ALT and AST), and histopathological changes were assessed. Additionally, molecular docking and dynamics were performed to evaluate the interaction of butin with target proteins. Butin treatment, at both doses, significantly restored biochemical parameters and preserved pancreatic histopathology in diabetic rats. It effectively modulated blood parameters, lipid profiles, inflammatory markers, apoptosis, antioxidant enzyme activity, oxidative stress, and hepatic markers. Molecular docking revealed that butin binds to proteins such as caspase-3 (1NME), NF-κB (1SVC), and serum insulin (4IBM) with binding affinities of - 7.4, - 6.5, and - 8.2 kcal/mol, respectively. Molecular dynamics simulations further suggested that butin induces significant conformational changes in these proteins. Butin exhibits potential effects against alloxan-induced diabetic rats by restoring biochemical balance, reducing inflammation, and protecting pancreatic tissue. Its binding to key proteins involved in apoptosis and inflammation highlights its therapeutic potential in diabetes management.

Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies

Self-association of therapeutic monoclonal antibodies (mabs) are thought to modulate the undesirably high viscosity observed in their concentrated solutions. Computational prediction of such a self-association behavior is advantageous early during mab drug candidate selection when material availability is limited. Here, we present a coarse-grained (CG) simulation method that enables microsecond molecular dynamics simulations of full-length antibodies at high concentrations. The proposed approach differs from others in two ways: first, charges are assigned to CG beads in an effort to reproduce molecular multipole moments and charge asymmetry of full-length antibodies instead of only localized charges. This leads to great improvements in the agreement between CG and all-atom electrostatic fields. Second, the distinctive hydrophobic character of each antibody is incorporated through empirical adjustments to the short-range van der Waals terms dictated by cosolvent all-atom molecular dynamics simulations of antibody variable regions. CG simulations performed on a set of 15 different mabs reveal that diffusion coefficients in crowded environments are markedly impacted by intermolecular interactions. Diffusion coefficients computed from the simulations are in correlation with experimentally measured observables, including viscosities at a high concentration. Further, we show that the evaluation of electrostatic and hydrophobic characters of the mabs is useful in predicting the nonuniform effect of salt on the viscosity of mab solutions. This CG modeling approach is particularly applicable as a material-free screening tool for selecting antibody candidates with desirable viscosity properties.

Development of a Structure-Based, pH-Dependent Lipophilicity Scale of Amino Acids from Continuum Solvation Calculations

Lipophilicity is a fundamental property to characterize the structure and function of proteins, motivating the development of lipophilicity scales. We report a versatile strategy to derive a pH-adapted scale that relies on theoretical estimates of distribution coefficients from conformational ensembles of amino acids. This is accomplished by using an accurately parametrized version of the IEFPCM/MST continuum solvation model as an effective way to describe the partitioning between n-octanol and water, in conjunction with a formalism that combines partition coefficients of neutral and ionic species of residues and the corresponding p K_a values of ionizable groups. Two weighting schemes are considered to derive solvent-like and protein-like scales, which have been calibrated by comparison with other experimental scales developed in different chemical/biological environments and pH conditions as well as by examining properties such as the retention time of small peptides and the recognition of antigenic peptides. A straightforward extension to nonstandard residues is enabled by this efficient methodological strategy.

Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic-level surface properties

The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic-level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three-dimensional structure. The applicability of these statistics for the prediction of protein retention behaviour was looked into. A linear combination of robust statistics describing the central tendency, heterogeneity and frequency of highly hydrophobic clusters was found to have a good predictive capability (R2  = 0.78), when combined a factor to account for protein size differences. The achieved error of prediction was 35% lower than for a similar model based on a description of the protein surface on an amino acid level. This indicates that a robust and mathematically simple model based on an atomic description of the protein surface can be used for the prediction of the retention behaviour of conformationally stable globular proteins with a well determined 3D structure in HIC. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:372-381, 2016.

Non-invasive high throughput approach for protein hydrophobicity determination based on surface tension

The surface hydrophobicity of a protein is an important factor for its interactions in solution and thus the outcome of its production process. Yet most of the methods are not able to evaluate the influence of these hydrophobic interactions under natural conditions. In the present work we have established a high resolution stalagmometric method for surface tension determination on a liquid handling station, which can cope with accuracy as well as high throughput requirements. Surface tensions could be derived with a low sample consumption (800 μL) and a high reproducibility (<0.1‰ for water) within a reasonable time (3.5 min per sample). This method was used as a non-invasive HTP compatible approach to determine surface tensions of protein solutions dependent on protein content. The protein influence on the solutions' surface tension was correlated to the hydrophobicity of lysozyme, human lysozyme, BSA, and α-lactalbumin. Differences in proteins' hydrophobic character depending on pH and species could be resolved. Within this work we have developed a pH dependent hydrophobicity ranking, which was found to be in good agreement with literature. For the studied pH range of 3-9 lysozyme from chicken egg white was identified to be the most hydrophilic. α-lactalbumin at pH 3 exhibited the most pronounced hydrophobic character. The stalagmometric method occurred to outclass the widely used spectrophotometric method with bromophenol blue sodium salt as it gave reasonable results without restrictions on pH and protein species.