Dataset Information

Versatile Lipases from the Candida rugosa-like Family: A Mechanistic Insight Using Computational Approaches.

ABSTRACT: Lipases are enzymes able to catalyze the hydrolysis or synthesis of triglycerides, depending on the reaction conditions, whereas sterol esterases show the same ability on sterol esters. Structurally, both kinds of enzymes display an α/β-hydrolase fold, with a substrate-binding pocket formed by a hydrophobic cavity covered by a mobile lid. However, it has been reported that some lipases from the Candida rugosa-like family display wide substrate specificity on both triglycerides and sterol esters. Among them, enzymes with different biotechnological applications, such as the lipase isoenzymes produced by C. rugosa and the sterol esterase from Ophiostoma piceae, have been exhaustively characterized and their crystal structures are available. Differences in substrate affinity among these proteins have been attributed to changes in their hydrophobicity. In this work, we analyzed the full catalytic mechanisms of these proteins using molecular dynamics tools, gaining insight into their mechanistic properties. In addition, we developed an in silico protocol to predict the substrate specificity using C. rugosa and O. piceae lipases as model enzymes and triglycerides and cholesterol esters with different fatty acid chain lengths as model substrates. The protocol was validated by comparing the in silico results with those described in the literature. These results would be useful to perform virtual screening of substrates for enzymes of the C. rugosa-like family with unknown catalytic properties.

SUBMITTER: Rodriguez-Salarichs J

PROVIDER: S-EPMC8479805 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Project description:An excessive amount of multidrug-resistant Staphylococcus aureus is commonly associated with actinic keratosis (AK) and squamous cell carcinoma (SCC) by secreted virulence products that induced the chronic inflammation leading to skin cancer which is regulated by staphylococcal accessory regulator (SarA). It is worth noting that there is currently no existing published study that reports on the inhibitory activity of phytochemicals derived from Santalum album on the SarA protein through in silico approach. Therefore, our study has been designed to find the potential inhibitors of S. aureus SarA protein from S. album-derived phytochemicals. The molecular docking study was performed targeting the SarA protein of S. aureus, and CID:5280441, CID:162350, and CID: 5281675 compounds showed the highest binding energy with -9.4 kcal/mol, -9.0 kcal/mol, and -8.6 kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period whereas the MMPBSA binding free energy proposed that the ligands were sustained with their binding site. All three complexes were found to be similar in distribution with the apoprotein through PCA analysis indicating conformational stability throughout the MD simulation. Moreover, all three compounds' ADMET profiles revealed positive results, and the AMES test did not show any toxicity whereas the pharmacophore study also indicates a closer match between the pharmacophore model and the compounds. After comprehensive in silico studies we evolved three best compounds, namely, Vitexin, Isovitexin, and Orientin, which were conducted in vitro assay for further confirmation of their inhibitory activity and results exhibited all of these compounds showed strong inhibitory activity against S. aureus. The overall result suggests that these compounds could be used as a natural lead to inhibit the pathogenesis of S. aureus and antibiotic therapy for S. aureus-associated skin cancer in humans as well.

Dataset Information

Versatile Lipases from the Candida rugosa-like Family: A Mechanistic Insight Using Computational Approaches.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets