Sulfur-containing amino acids, Methionine (Met) and Cysteine (Cys), are very susceptible to Reactive Oxygen Species (ROS). Therefore, sulfur-based reactions regulate many biological processes, playing a key role in maintaining cellular redox homeostasis and modulating intracellular signaling cascades. In oxidative conditions, Met acts as a ROS scavenger, through Met sulfoxide formation, while thiol/disulfide interchange reactions take place between Cys residues as a response to many environmental stimuli. In this work, we apply a QM/MM theoretical–computational approach, which combines quantum–mechanical calculations with classical molecular dynamics simulations to estimate the free energy profile for the above-mentioned reactions in solution. The results obtained, in good agreement with experimental data, show the validity of our approach in modeling sulfur-based reactions, enabling us to study these mechanisms in more complex biological systems.

TipA, a MerR family transcription factor from Streptomyces lividans , promotes antibiotic resistance by sequestering broad-spectrum thiopeptide-based antibiotics, thus counteracting their inhibitory effect on ribosomes. TipAS, a minimal binding motif which is expressed as an isoform of TipA, harbors a partially disordered N-terminal subdomain that folds upon binding multiple antibiotics. The extent and nature of the underlying molecular heterogeneity in TipAS that shapes its promiscuous folding–function landscape is an open question and is critical for understanding antibiotic-sequestration mechanisms. Here, combining equilibrium and time-resolved experiments, statistical modeling, and simulations, we show that the TipAS native ensemble exhibits a pre-equilibrium between binding-incompetent and binding-competent substates, with the fully folded state appearing only as an excited state under physiological conditions. The binding-competent state characterized by a partially structured N-terminal subdomain loses structure progressively in the physiological range of temperatures, swells on temperature increase, and displays slow conformational exchange across multiple conformations. Binding to the bactericidal antibiotic thiostrepton follows a combination of induced-fit and conformational-selection-like mechanisms, via partial binding and concomitant stabilization of the binding-competent substate. These ensemble features are evolutionarily conserved across orthologs from select bacteria that infect humans, underscoring the functional role of partial disorder in the native ensemble of antibiotic-sequestering proteins belonging to the MerR family.

Cytochrome P450 OleP catalytic activity is strongly influenced by its structural dynamic conformational behavior. Here, we combine equilibrium-binding experiments with all-atom molecular dynamics simulations to clarify how different environments affect OleP conformational equilibrium between the open and the closed—catalytic competent—forms. Our data clearly show that at high-ionic strength conditions, the closed form is favored, and, very interestingly, different mechanisms, depending on the chemistry of the cations, can be used to rationalize such an effect.

The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R’s three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research.

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the immunoglobulin framework (FW), which provides structural rigidity and support for the flexible CDR loops. Here we present an integrated computational-experimental workflow, combining static structure analyses, molecular dynamics simulations and in vitro physicochemical and functional assays to generate rational designs of FW mutations for modulating antibody stability and activity. We first showed that recent antibody-specific language models lacked insights in FW mutagenesis, in comparison to approaches that use antibody structure information. Using the widely used breast cancer therapeutic trastuzumab as a use case, we designed stabilizing mutants which were distal to the CDR and preserved the antibody's functionality to engage its cognate antigen (HER2) and induce antibody-dependent cellular cytotoxicity. Interestingly, guided by local backbone motions predicted using molecular dynamics simulations, we designed a FW mutation on the trastuzumab light chain that retained antigen-binding effects, but lost Fab-mediated and Fc-mediated effector functions. This highlighted the effects of FW on immunological functions engendered in distal areas of the antibody, and the importance of considering attributes other than binding affinity when assessing antibody function. Our approach incorporates interdomain dynamics and distal effects between FW and the Fc domains, expands the scope of antibody engineering beyond the CDR, and underscores the importance of a holistic perspective that considers the entire antibody structure in optimizing antibody stability, developability and function.