Rafael C. Bernardi

While overlooked during the Human Microbiome Project, characterizing the urinary microbiota in health and disease is a new frontier in microbiome science. Recent studies have associated differential abundance of bacterial taxa including Propionimicrobium lymphophilum and Actinobaculum/Actinotignum spp. with prostate cancer. In this study, we collected urine from subjects before prostate biopsy and applied a Human Sterolbiome Discovery High-throughput (HSDH) assay to identify culturable urinary bacteria with the ability to generate androgens. Application of the HSDH Assay to urine samples led to the isolation of eight P. lymphophilum strains positive for cortisol side-chain cleavage (steroid-17,20-desmolase), 17β-HSDH activity, or both. In addition, we isolated three strains of Actinobaculum massiliense that carry the DHEA isomerase reductase (dir) genes. The dirA gene encodes a multifunctional 3β/17β-hydroxysteroid dehydrogenase/Δ^4,5-isomerase and the dirB gene encodes a 17β-hydroxysteroid dehydrogenase isoform. Structural prediction and molecular dynamics reveal probable catalytic mechanisms based on the shared catalytic triad but distinct binding-pocket geometries of DirA and DirB, which enabled predictions of their respective reactions. Phylogenetic analysis of DirA and DirB revealed homologs in urinary tract commensals and in bacteria associated with steroid degradation in aquatic and terrestrial environments. Taken together, the development of the HSDH Assay and the identification of the dir pathway genes provide the methodological foundation and provide the molecular basis for advancing our understanding of the role of urinary tract bacteria in host endocrine physiology.

Kinases are major drug targets especially in cancer therapy. However, the high degree of conservation of their active sites hinders the development of selective inhibitors, motivating a deeper understanding of kinase conformational ensembles and allosteric communication pathways. Here, we use dynamical network analysis to identify key residues involved in a dynamic allostery between the N- and C-lobes that connects the major functional units of the MAP kinase p38α. By combining NMR spectroscopy, activity assays, and in silico analysis of wildtype protein and mutants in the presence or absence of an active-site inhibitor, we experimentally validate the obtained architecture with respect to global protein motion and long-range allosteric modulation. Notably, the identified network highlights communication pathways across several functional sites, prominently involving the allosteric site, the activation loop, and even the lipid-binding domain with its embedded cryptic pocket in the C-lobe. These findings provide mechanistic insight into p38α allostery and suggest viable opportunities for the rational design of allosteric modulators of MAP kinases.

Kinases are major drug targets especially in cancer therapy. However, the high degree of conservation of their active sites hinders the development of selective inhibitors, motivating a deeper understanding of kinase conformational ensembles and allosteric communication pathways. Here, we use dynamical network analysis to identify key residues involved in a dynamic allostery between the N- and C-lobes that connects the major functional units of the MAP kinase p38α. By combining NMR spectroscopy, activity assays, and in silico analysis of wildtype protein and mutants in the presence or absence of an active-site inhibitor, we experimentally validate the obtained architecture with respect to global protein motion and long-range allosteric modulation. Notably, the identified network highlights communication pathways across several functional sites, prominently involving the allosteric site, the activation loop, and the lipid-binding domain with its embedded cryptic pocket in the C-lobe. These findings provide mechanistic insight into p38α allostery and suggest new opportunities for the rational design of allosteric modulators of MAP kinases.

The simulation of biological systems has undergone a revolutionary transformation, progressing from modeling single proteins to entire cellular environments. This leap forward is driven by the convergence of molecular dynamics (MD) simulations and artificial intelligence (AI)-powered structure prediction. Traditionally, MD simulations provided atomic-level insights into protein function and interactions, yet their accuracy relied on experimentally determined structures. AI-based models, such as AlphaFold, now enable the rapid and accurate prediction of protein structures, expanding the scope of simulations beyond isolated biomolecules to complex assemblies. However, a structure alone is not sufficient to capture biological function. Molecular motion underlies almost all cellular processes, from enzyme catalysis to signal transduction. MD simulations breathe life into static models, revealing dynamic conformational changes and mechanistic pathways. With computational power and AI capabilities, we are now approaching the long-sought goal of simulating entire cellular processes with unprecedented resolution. This chapter explores how AI and MD are bridging the gap between static snapshots and dynamic cellular models, paving the way for whole-cell simulations. The ability to computationally reconstruct cellular behavior at the molecular scale is poised to transform biological research, drug discovery, and synthetic biology, marking an era in which digital cells become a fundamental tool in scientific exploration.

Protein–protein docking is a cornerstone of computational structural biology, yet its reliability for large, multimeric assemblies remains uncertain. Standard workflows typically include geometry optimization or molecular dynamics equilibration to relieve local strains and improve input quality, but the extent to which these preparatory steps alter docking outcomes has not been systematically evaluated. Here, we address this question using the mitochondrial chaperonin Hsp60, a dynamic double-ring complex essential for protein folding, and MIX, a kinetoplastid-specific protein with unresolved function, as a stress test system. By comparing docking predictions across minimized, equilibrated, and ensemble-refined structures of Hsp60 in three conformational states (apo, ATP-bound, and ATP–Hsp10), we show that structural relaxation profoundly reshapes the docking landscape. Minimization alone often yielded favorable scores but localized binding, while longer MD trajectories exposed alternative sites, including central cavity, equatorial ATP pocket, and apical domain, each consistent with distinct regulatory hypotheses. These findings reveal that docking outcomes are highly sensitive to receptor preparation, especially in complexes undergoing large conformational transitions. More broadly, our study highlights an underappreciated vulnerability of docking pipelines and calls for ensemble-based and dynamics-aware approaches when predicting interactions in large biomolecular machines.

Mechanically resilient protein interactions are crucial for biological processes ranging from bacterial adhesion to human tissue formation. Catch-bonds, a unique class of protein interactions that strengthen under force, act like a molecular finger trap, tightening to prevent bond rupture. However, it remains unclear whether catch-bonds form immediately upon force application or require a specific force threshold for stabilization. Here, we employ an in silico single-molecule force spectroscopy approach that combines molecular dynamics (MD) simulations, dynamical network analysis, and AI-based modeling to investigate the XDoc:CohE complex, a hyperstable catch-bond found in cellulose-degrading bacteria. By analyzing amino acid interactions between XDoc and cohesin E, and between XDoc submodules (X-module and Doc), we show that AI regression models can accurately predict rupture forces using only short MD simulations, capturing key mechanostability features despite the binding interface's complexity. Our results reveal that mechanostability signatures emerge early under force load, indicating that catch-bonds activate almost immediately. These findings provide new insights into the molecular principles governing force-dependent protein interactions and highlight the potential of AI-driven approaches for predicting and characterizing mechanostability, with broad implications for bioengineering and drug design.

Calcium is a critical regulator of Staphylococcus aureus skin adhesion, stabilizing one of the strongest noncovalent biomolecular interactions ever recorded. Using in vitro and in silico single-molecule force spectroscopy, we demonstrate that calcium ions (Ca ²⁺ ) are essential for the ultrastrong binding between the serine-aspartate repeat protein D (SdrD) adhesin and the human skin protein desmoglein-1 (DSG-1), withstanding forces exceeding 2 nanonewtons. Ca ²⁺ ions stabilize both the SdrD complex and the mechanically robust SdrD B-domains, which exhibit unprecedented folding strength. In the context of atopic dermatitis (AD), disrupted calcium gradients amplify SdrD interactions, which could potentially intensify S. aureus virulence. Furthermore, abnormal DSG-1 distribution on AD-affected skin enhances bacterial adhesion. These findings provide crucial insights into the calcium-dependent regulation of bacterial adhesion and folding, suggesting possible therapeutic targets to combat S. aureus infections.

Commensal bacteria have been implicated in the modulation of steroid hormones, including circulating androgen levels in the host. However, the microbial genetic pathways involved in androgen production have not been fully characterized. Here we identify a microbial gene encoding an enzyme that catalyses the conversion of androstenedione to epitestosterone in the gut microbiome member Clostridium scindens and named this gene desF. We demonstrate that epitestosterone impacts androgen receptor-dependent prostate cancer cell proliferation in vitro. We also demonstrate that stool desF levels are elevated in patients with prostate cancer who are unresponsive to abiraterone/prednisone therapy. Bacterial isolates from urine or prostatectomy tissue produced androgens, and 17β-hydroxysteroid dehydrogenase activity encoded by the desG gene was detected in strains of the urinary tract bacterium Propionimicrobium lymphophilum. Furthermore, we demonstrate that urinary androgen-producing bacterial strains can promote prostate cancer cell growth through metabolism of cortisol and prednisone. Abiraterone, which targets host desmolase (CYP17A1), a rate-limiting enzyme in adrenal steroidogenesis, does not inhibit bacterial desmolase (DesAB), whereas the conversion of prednisone to androgens by DesAB, DesF and DesG drives androgen-receptor-dependent prostate cancer cell line proliferation in vitro. Our results are a significant advance in steroid microbiology and highlight a potentially important role for gut and urinary tract bacteria in host endocrine function and drug metabolism.

Chagas disease, caused by the protozoan Trypanosoma cruzi, affects millions globally, leading to severe cardiac and gastrointestinal complications in its chronic phase. The invasion of host cells by T. cruzi is mediated by the interaction between the parasite's glycoprotein gp82 and the human receptor lysosome-associated membrane protein 2 (LAMP2). While experimental studies have identified a few residues involved in this interaction, a comprehensive molecular-level understanding has been lacking. In this study, we present a 1.44-million-atom computational model of the gp82 complex, including over 3300 lipids, glycosylation sites, and full molecular representations of gp82 and LAMP2, making it the most complete model of a parasite-host interaction to date. Using microsecond-long molecular dynamics simulations and dynamic network analysis, we identified critical residue interactions, including novel regions of contact that were previously uncharacterized. Our findings also highlight the significance of the transmembrane domain of LAMP2 in stabilizing the complex. These insights extend beyond traditional hydrogen bond interactions, revealing a complex network of cooperative motions that facilitate T. cruzi invasion. This study not only confirms key experimental observations but also uncovers new molecular targets for therapeutic intervention, offering a potential pathway to disrupt T. cruzi infection and combat Chagas disease.

The SARS-CoV-2 spike protein’s membrane-binding domain bridges the viral and host cell membrane, a critical step in triggering membrane fusion. Here, we investigate how the SARS-CoV-2 spike protein interacts with host cell membranes, focusing on a membrane-binding peptide (MBP) located near the TMPRSS2 cleavage site. Through in vitro and computational studies, we examine both primed (TMPRSS2-cleaved) and unprimed versions of the MBP, as well as the influence of its conserved disulfide bridge on membrane binding. Our results show that the MBP preferentially associates with cholesterol-rich membranes, and we find that cholesterol depletion significantly reduces viral infectivity. Furthermore, we observe that the disulfide bridge stabilizes the MBP’s interaction with the membrane, suggesting a structural role in viral entry. Together, these findings highlight the importance of membrane composition and peptide structure in SARS-CoV-2 infectivity and suggest that targeting the disulfide bridge could provide a therapeutic strategy against infection.