Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase

Decoding the mechanism governing the structural stability of wheat germ agglutinin and its isolated domains: A combined calorimetric, NMR, and MD simulation study

Wheat germ agglutinin (WGA) demonstrates potential as an oral delivery agent owing to its selective binding to carbohydrates and its capacity to traverse biological membranes. In this study, we employed differential scanning calorimetry and molecular dynamics simulations to comprehensively characterize the thermal unfolding process of both the complete lectin and its four isolated domains. Furthermore, we present the nuclear magnetic resonance structures of three domains that were previously lacking experimental structures in their isolated forms. Our results provide a collective understanding of the energetic and structural factors governing the intricate unfolding mechanism of the complete agglutinin, shedding light on the specific role played by each domain in this process. The analysis revealed negligible interdomain cooperativity, highlighting instead significant coupling between dimer dissociation and the unfolding of the more labile domains. By comparing the dominant interactions, we rationalized the stability differences among the domains. Understanding the structural stability of WGA opens avenues for enhanced drug delivery strategies, underscoring its potential as a promising carrier throughout the gastrointestinal environment.

A 2022 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides

The antimicrobial resistance process has been accelerated by the over-prescription and misuse of antibiotics [...].

Exploring the druggability of the binding site of aurovertin, an exogenous allosteric inhibitor of FOF1-ATP synthase

In addition to playing a central role in the mitochondria as the main producer of ATP, F_OF₁-ATP synthase performs diverse key regulatory functions in the cell membrane. Its malfunction has been linked to a growing number of human diseases, including hypertension, atherosclerosis, cancer, and some neurodegenerative, autoimmune, and aging diseases. Furthermore, inhibition of this enzyme jeopardizes the survival of several bacterial pathogens of public health concern. Therefore, F_OF₁-ATP synthase has emerged as a novel drug target both to treat human diseases and to combat antibiotic resistance. In this work, we carried out a computational characterization of the binding sites of the fungal antibiotic aurovertin in the bovine F₁ subcomplex, which shares a large identity with the human enzyme. Molecular dynamics simulations showed that although the binding sites can be described as preformed, the inhibitor hinders inter-subunit communications and exerts long-range effects on the dynamics of the catalytic site residues. End-point binding free energy calculations revealed hot spot residues for aurovertin recognition. These residues were also relevant to stabilize solvent sites determined from mixed-solvent molecular dynamics, which mimic the interaction between aurovertin and the enzyme, and could be used as pharmacophore constraints in virtual screening campaigns. To explore the possibility of finding species-specific inhibitors targeting the aurovertin binding site, we performed free energy calculations for two bacterial enzymes with experimentally solved 3D structures. Finally, an analysis of bacterial sequences was carried out to determine conservation of the aurovertin binding site. Taken together, our results constitute a first step in paving the way for structure-based development of new allosteric drugs targeting F_OF₁-ATP synthase sites of exogenous inhibitors.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.