Biology subjects

Melo, M. C. R.

Publications and source records attributed to Melo, M. C. R..

5 recordsLinked to original sources

bioRxiv2023

Effects of Variants of Concern Mutations on the Force-Stability of the SARS-CoV-2:ACE2 Interface and Virus Transmissibility

Viruses mutate under a variety of selection pressures, allowing them to continuously adapt to their hosts. Mutations in SARS-CoV-2 have shown effective evasion of population immunity and increased affinity to host factors, in particular to the cellular receptor ACE2. However, in the dynamic environment of the respiratory tract forces act on the binding partners, which raises the question whether not only affinity, but also force-stability of the SARS-CoV-2:ACE2 bond, might be a selection factor for mutations. Here, we use magnetic tweezers (MT) to study the effect of amino acid substitutions in variants of concern (VOCs) on RBD:ACE2 bond kinetics with and without external load. We find higher affinity for all VOCs compared to wt, in good agreement with previous affinity measurements in bulk. In contrast, Alpha is the only VOC that shows significantly higher force stability compared to wt. Investigating the RBD:ACE2 interactions with molecular dynamics simulations, we are able to rationalize the mechanistic molecular origins of this increase in force-stability. Our study emphasizes the diversity of contributions to the assertiveness of variants and establishes force-stability as one of several factors for fitness. Understanding fitness-advantages opens the possibility for prediction of likely mutations allowing rapid adjustment of therapeutics, vaccination, and intervention measures.

biophysics↗

bioRxiv2022

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning

Molecular de-extinction could offer new avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted as subsequences of extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides were tested for mechanism of action, resistance to proteolysis, and anti-infective efficacy in two pre-clinical mouse models. These results suggest that machine learning-based encrypted peptide prospection can identify stable, nontoxic antimicrobial peptides. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery. HighlightsO_LIMachine learning guides bioinspired prospection for encrypted antimicrobial peptides. C_LIO_LIModern and extinct human proteins harbor antimicrobial subsequences. C_LIO_LIAncient encrypted peptides display in vitro and in vivo activity with low host toxicity. C_LIO_LIPaleoproteome mining offers a new framework for antibiotic discovery. C_LI

bioengineering↗

bioRxiv2022

Bridging the gab between in vitro and in silico single-molecule force spectroscopy

Staphylococci bacteria use an arsenal of virulence factors, mainly composed of proteins such as adhesins, to target and adhere to their host. Adhesins play critical roles during infection, mainly during the early steps of adhesion when cells are exposed to high mechanical stress. S. epidermidis SdrG:Fg{beta} force resilience has been investigated using AFM-based single molecule force spectroscopy experiments paired with steered molecular dynamics (SMD) simulations. However, there is still a gap between both kinds of experiments at high force-loading rates. Here, we leveraged the high-speed of coarse-grained (CG) SMD simulations to bridge the gap between the data obtained in vitro and in silico with all-atom SMD. We used the DHS theory to connect the two types of SMD simulations and the predictions are consistent with theory and experimentation. We believe that, when associated with all-atom SMD, course-grained SMD can be a powerful ally to help explain and complement the results of single-molecule force spectroscopy experiments.

biophysics↗

bioRxiv2020

Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories

Molecular interactions are essential for regulation of cellular processes, from the formation of multiprotein complexes, to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions, or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the Dynamical Network Analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is generally limited to relatively small biomolecular systems. In this work we provide an evolution of the method, application and interface. All data processing and analysis is conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination optimal paths. Using the popular visualization program VMD, high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5 M atoms, namely the OMP-decarboxylase, the Leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and the respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

biophysics↗

bioRxiv2020

Bacteria on steroids: the enzymatic mechanism of an NADH-dependent dehydrogenase that regulates the conversion of cortisol to androgen in the gut microbiome

Microorganisms extensively modify host steroids, but whether these reactions merely eliminate hormones or create signals with new biological identities is largely unknown. Gut bacteria have been known for more than four decades to reduce cortisol to 20-dihydrocortisol, yet the physiological consequence of this transformation remained unresolved. Here we show that microbial cortisol reduction creates a distinct host signalling molecule. A 2.0-[A] structure of the bacterial enzyme DesC, together with molecular dynamics, biochemical perturbation and hybrid quantum mechanics/molecular mechanics simulations, defines substrate recognition and an ordered hydride-transfer and proton-relay mechanism. In gnotobiotic mice, isogenic bacteria expressing active DesC--but not a catalytically inactive S47A variant produced 20-dihydrocortisol in the intestine and circulation and reprogrammed colonic transcription. In primary intestinal epithelial cells, 20-dihydrocortisol, but not cortisol, activated ERK-dependent inflammatory and growth-associated programmes. These responses required nuclear receptor subfamily 4 group A member 3 (NR4A3), whose purified ligand-binding domain bound 20-dihydrocortisol but showed no detectable binding to cortisol. A Chicago colonoscopy cohort linked chronic cortisol exposure, faecal desC, the microbial metabolite and colorectal phenotypes. Thus, bacterial metabolism can change receptor selectivity and biological activity rather than simply terminate host hormone action, expanding the endocrine chemistry of the host.

microbiology↗