In silico design of fusion keratinocyte growth factor containing collagen-binding domain for tissue engineering application

Homology modeling and thermostability enhancement of Vibrio palustris PETase via hydrophobic interactions

The quest for sustainable solutions to plastic pollution has driven research into plastic-degrading enzymes, offering promising avenues for polymer recycling applications. However, enzymes derived from natural sources often exhibit suboptimal thermostability, hindering their industrial viability. Protein engineering techniques have emerged as a powerful approach to enhance the desired properties of these biocatalysts. This study aims to conduct a comprehensive analysis of the thermostability of Vibrio palustris PETase (VpPETase) through an integrated computational approach encompassing homology modeling, site-specific molecular docking, molecular dynamics (MD) simulations, and comparative evaluation of a single-point mutation (V195F) against the wild-type enzyme. Homology modeling was used to predict VpPETase model using multiple templates. Model quality was rigorously assessed using Ramachandran plot analysis, ProSA, Verify 3D, and ERRAT. Molecular docking elucidated the catalytic region comprising residues His149, Asp117, and Ser71, while highlighting the pivotal roles of His149, Tyr1, and Ser71 in substrate binding affinity. MD simulations at various temperatures revealed higher stability at 313.15 K over a 100 ns trajectory, as evidenced by analyses of root-mean-square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), hydrogen bonding, and root-mean-square fluctuation (RMSF). The V195F mutant exhibited a slight increase in stability compared to wild-type. While this study provides valuable insights into the thermostability of VpPETase, further investigations, including experimental validation of thermostability enhancements and in vitro characterization, are warranted to fully exploit the potential of this enzyme for industrial applications in plastic recycling.

In Silico Structural Insights into a Glucanase from Clostridium perfringens and Prediction of Structural Stability Improvement Through Hydrophobic Interaction Network and Aromatic Interaction

Glucanases are widely applied in industrial applications such as brewing, biomass conversion, food, and animal feed. Glucanases catalyze the hydrolysis of glucan to produce the sugar hemiacetal through hydrolytic cleavage of glycosidic bonds. Current study aimed to investigate structural insights of a glucanase from Clostridium perfringens through blind molecular docking, site-specific molecular docking, molecular dynamics (MD) simulation, and binding energy calculation. Furthermore, we aimed to enhance structural stabilization through formation of hydrophobic interaction network. The molecular docking results illustrated that residues Glu222 and Asp187 may act as nucleophile acid/base catalyst. Moreover, the MM/PBSA results illustrated a high binding affinity of 108.71 ± 8.5 kJ/mol between glucanase and barely glucan during 100 ns simulation. The RMSF analysis illustrated a high flexible surface loop with the highest mobility at position D130. Therefore, the structural engineering was carried out through introducing a double-mutant S125Y/D130P, and the structural stability was improved by forming the hydrophobic interaction network and one π-π aromatic interaction. The spatial distance between the mutation sites and the catalytic pocket attenuates their direct impact on binding interactions within the catalytic pocket.

Understanding the ideal wound healing mechanistic behavior using in silico modelling perspectives: A review

Complexity of the entire body precludes an accurate assessment of the specific contributions of tissues or cells during the healing process, which might be expensive and time consuming. Because of this, controlling the wound's size, depth, and dimensions may be challenging, and there is not yet an efficient and reliable chronic wound model representation. Furthermore, given the inherent challenges associated with conducting non-invasive in vivo investigations, it becomes peremptory to explore alternative methodologies for studying wound healing. In this context, biologically-realistic mathematical and computational models emerge as a valuable framework that can effectively address this need. Therefore, it might improve our approach to understanding the process at its core. This article will examines all facets of wound healing, including the kinds, pathways, and most current developments in wound treatment worldwide, particularly in silico modelling utilizing both mathematical and structure-based modelling techniques. It may be helpful to identify the crucial traits through the feedback loop of computer models and experimental investigations in order to build innovative therapies to cure wounds. Hence the effectiveness of personalised medicine and more targeted therapy in the healing of wounds may be enhanced by this interdisciplinary expertise.

Therapeutic Fusion Proteins

Therapeutic fusion proteins are a class of hybrid constructs that combine distinct biomolecules into a single platform with the additive effects of the components. The ability to fuse two unrelated proteins provides a means to localize mechanisms to better treat a range of diseases. Fusion proteins can be designed to impart diverse functions, including increasing half-life, providing targeting, and enabling sustained signaling. Of these, half-life extenders, which are fused to a therapeutic protein to increase exposure, are the most established group of fusion proteins, with many clinical successes. Rapid advances in antibody and antibody-derivative technology have enabled the fusion of targeting domains with therapeutic proteins. An emerging group of therapeutic fusion proteins has two separate active functions. Although most research for therapeutic fusion proteins focuses on cancer, prior successes provide a foundation for studies into other diseases as well. The exponential emergence of biopharmaceuticals gives precedence for increased research into therapeutic fusion proteins for a multitude of diseases.

Characterization of LL37 Binding to Collagen through Peptide Modification with a Collagen-Binding Domain

Collagen-based biomaterials loaded with antimicrobial peptides (AMPs) present a promising approach for promoting wound healing while providing protection against infections. In our previous work, we modified the AMP LL37 by incorporating a collagen-binding domain (cCBD) as an anchoring unit for collagen-based wound dressings. We demonstrated that cCBD-modified LL37 (cCBD-LL37) exhibited improved retention on collagen after washing with PBS. However, the binding mechanism of cCBD-LL37 to collagen remained to be elucidated. In this study, we found that cCBD-LL37 showed a slightly higher affinity for collagen compared to LL37. Our results indicated that cCBD inhibited cCBD-LL37 binding to collagen but did not fully eliminate the binding. This suggests that cCBD-LL37 binding to collagen may involve more than just one-site-specific binding through the collagen-binding domain, with non-specific interactions also playing a role. Electrostatic studies revealed that both LL37 and cCBD-LL37 interact with collagen via long-range electrostatic forces, initiating low-affinity binding that transitions to close-range or hydrophobic interactions. Circular dichroism analysis showed that cCBD-LL37 exhibited enhanced structural stability compared to LL37 under varying ionic strengths and pH conditions, implying potential improvements in antimicrobial activity. Moreover, we demonstrated that the release of LL37 and cCBD-LL37 into the surrounding medium was influenced by the electrostatic environment, but cCBD could enhance the retention of peptide on collagen scaffolds. Collectively, these results provide important insights into cCBD-modified AMP-binding mechanisms and suggest that the addition of cCBD may enhance peptide structural stability and retention under varying electrostatic conditions.