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The expression of antibiotic-inactivating enzymes, such as Pseudomonas -derived cephalosporinase-3 (PDC-3), is a major mechanism of intrinsic resistance in bacteria. Using reinforcement learning-driven molecular dynamics simulations and constant pH MD, we investigate how clinically observed mutations in the Ω-loop (at residues V211, G214, E219, and Y221) alter the structure and function of PDC-3. Our findings reveal that these substitutions modulate the dynamic flexibility of the Ω-loop and the R2-loop, reshaping the cavity of the active site. In particular, E219K and Y221A disrupt the tridentate hydrogen bond network around K67, thus lowering its pKa and promoting proton transfer to the catalytic residue S64. Markov state models reveal that E219K achieves enhanced catalysis by adopting stable, long-lived ‘active’ conformations, whereas Y221A facilitates activity by rapidly toggling between bond-formed and bond-broken states. In addition, substitutions influence key hydrogen bonds that control the opening and closure of the active-site pocket, consequently influencing the overall size. The pocket expands in all nine clinically identified variants, creating additional space to accommodate bulkier R1 and R2 cephalosporin side chains. Taken together, these results provide a mechanistic basis for how single residue substitutions in the Ω-loop affect catalytic activity. Insights into the structural dynamics of the catalytic site advance our understanding of emerging β -lactamase variants and can inform the rational design of novel inhibitors to combat drug-resistant P. aeruginosa .

Exome sequencing of 2,871 probands with congenital heart disease (CHD) provides new insights into the genetic architecture of these disorders. The results implicate new genes in CHD pathogenesis and highlight striking overlap between genes with damaging de novo mutations in individuals with CHD and autism. Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent–offspring trios, implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for ∼5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for ∼11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including ∼3% of isolated CHD patients and ∼28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in ∼440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.

Understanding allostery in enzymes and tools to identify it offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover, and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences. Antibiotics are crucial drugs for treating and preventing bacterial infections, but some bacteria are evolving ways to resist their effects. This ‘antibiotic resistance’ threatens lives and livelihoods worldwide. β-lactam antibiotics, like penicillin, are some of the most commonly used, but some bacteria can now make enzymes called β-lactamases, which destroy these antibiotics. Dozens of different types of β-lactamases now exist, each with different properties. Two of the most medically important are TEM-1 and KPC-2. One way to counteract β-lactamases is with drugs called inhibitors that stop the activity of these enzymes. The approved β-lactamase inhibitors work by blocking the part of the enzyme that binds and destroys antibiotics, known as the 'active site'. The β-lactamases have evolved, some of which have the ability to resist the effects of known inhibitors. It is possible that targeting parts of β-lactamases far from the active site, known as 'allosteric sites', might get around these new bacterial defences. A molecule that binds to an allosteric site might alter the enzyme's shape, or restrict its movement, making it unable to do its job. Galdadas, Qu et al. used simulations to understand how molecules binding at allosteric sites affect enzyme movement. The experiments examined the structures of both TEM-1 and KPC-2, looking at how their shapes changed as molecules were removed from the allosteric site. This revealed how the allosteric sites and the active site are linked together. When molecules were taken out of the allosteric sites, they triggered ripples of shape change that travelled via loop-like structures across the surface of the enzyme. These loops contain over half of the known differences between the different types of β-lactamases, suggesting mutations here may be responsible for changing which antibiotics each enzyme can destroy. In other words, changes in the 'ripples' may be related to the ability of the enzymes to resist particular antibiotics. Understanding how changes in one part of a β-lactamase enzyme reach the active site could help in the design of new inhibitors. It might also help to explain how β-lactamases evolve new properties. Further work could show why different enzymes are more or less active against different antibiotics.

Non-structural protein 1 (Nsp1) is a main pathogenicity factor of α - and β - coronaviruses. Nsp1 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suppresses the host gene expression by sterically blocking 40S host ribosomal subunits and promoting host mRNA degradation. This mechanism leads to the downregulation of the translation-mediated innate immune response in host cells, ultimately mediating the observed immune evasion capabilities of SARS-CoV-2. Here, by combining extensive molecular dynamics simulations, fragment screening and crystallography, we reveal druggable pockets in Nsp1. Structural and computational solvent mapping analyses indicate the partial crypticity of these newly discovered and druggable binding sites. The results of fragment-based screening via X-ray crystallography confirm the druggability of the major pocket of Nsp1. Finally, we show how the targeting of this pocket could disrupt the Nsp1-mRNA complex and open a novel avenue to design new inhibitors for other Nsp1s present in homologous β - coronaviruses.

Mutations within the kinase domain of the epidermal growth factor receptor (EGFR) are common oncogenic driver events in non-small cell lung cancer. Although the activation of EGFR in normal cells is primarily driven by growth-factor-binding-induced dimerization, mutations on different exons of the kinase domain of the receptor have been found to affect the equilibrium between its active and inactive conformations giving rise to growth-factor-independent kinase activation. Using molecular dynamics simulations combined with enhanced sampling techniques, we compare here the conformational landscape of the monomers and homodimers of the wild-type and mutated forms of EGFR ΔELREA and L858R, as well as of two exon 20 insertions, D770-N771insNPG, and A763-Y764insFQEA. The differences in the conformational energy landscapes are consistent with multiple mechanisms of action including the regulation of the hinge motion, the stabilization of the dimeric interface, and local unfolding transitions. Overall, a combination of different effects is caused by the mutations and leads to the observed aberrant signaling.

RNA-targeting compounds or small interfering RNAs (siRNAs) offer a potent means to control viral infections. An essential prerequisite for their design depends on identifying conserved and functional viral RNA structures in cells. Techniques that probe RNA structures in situ have recently been developed including SHAPE-MaP, which has been helpful in the analysis of secondary structures of RNA. In this study, we report on the application of SHAPE-MaP to the porcine epidemic diarrhea virus RNA genome to categorize different functional regions, including potential quadruplex-forming sequence and target sites of siRNA. Our results show that these structures can be exploited to inhibit viral proliferation and that SHAPE-MaP is an effective method to identify secondary structures in RNA genomes.

The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins’ performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue–residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings—residue pairs with critical dynamical interactions that have been preserved during evolution—DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.

Congenital hydrocephalus (CH), characterized by enlarged brain ventricles, is considered a disease of excessive cerebrospinal fluid (CSF) accumulation and thereby treated with neurosurgical CSF diversion with high morbidity and failure rates. The poor neurodevelopmental outcomes and persistence of ventriculomegaly in some post-surgical patients highlight our limited knowledge of disease mechanisms. Through whole-exome sequencing of 381 patients (232 trios) with sporadic, neurosurgically treated CH, we found that damaging de novo mutations account for >17% of cases, with five different genes exhibiting a significant de novo mutation burden. In all, rare, damaging mutations with large effect contributed to ~22% of sporadic CH cases. Multiple CH genes are key regulators of neural stem cell biology and converge in human transcriptional networks and cell types pertinent for fetal neuro-gliogenesis. These data implicate genetic disruption of early brain development, not impaired CSF dynamics, as the primary pathomechanism of a significant number of patients with sporadic CH. The largest whole-exome sequencing study of sporadic congenital hydrocephalus identities mutations associated with disrupted fetal neuro-gliogenesis as the primary pathophysiological event in a significant number of cases.

The insulin-linked polymorphic region is a variable number of tandem repeats region of DNA in the promoter of the insulin gene that regulates transcription of insulin. This region is known to form the alternative DNA structures, i-motifs and G-quadruplexes. Individuals have different sequence variants of tandem repeats and although previous work investigated the effects of some variants on G-quadruplex formation, there is not a clear picture of the relationship between the sequence diversity, the DNA structures formed, and the functional effects on insulin gene expression. Here we show that different sequence variants of the insulin linked polymorphic region form different DNA structures in vitro. Additionally, reporter genes in cellulo indicate that insulin expression may change depending on which DNA structures form. We report the crystal structure and dynamics of an intramolecular i-motif, which reveal sequences within the loop regions forming additional stabilising interactions that are critical to formation of stable i-motif structures. The outcomes of this work reveal the detail in formation of stable i-motif DNA structures, with potential for rational based drug design for compounds to target i-motif DNA. DNA sequences from the insulin-linked polymorphic region can form non-canonical structures. Here, the authors present a structural investigation into the relationship between native sequence variants and the different structures they form.

The coronavirus SARS-CoV-2 protects its RNA from being recognized by host immune responses by methylation of its 5’ end, also known as capping. This process is carried out by two enzymes, non-structural protein 16 (NSP16) containing 2’-O-methyltransferase and NSP14 through its N7 methyltransferase activity, which are essential for the replication of the viral genome as well as evading the host’s innate immunity. NSP10 acts as a crucial cofactor and stimulator of NSP14 and NSP16. To further understand the role of NSP10, we carried out a comprehensive analysis of >13 million globally collected whole-genome sequences (WGS) of SARS-CoV-2 obtained from the Global Initiative Sharing All Influenza Data (GISAID) and compared it with the reference genome Wuhan/WIV04/2019 to identify all currently known variants in NSP10. T12I, T102I, and A104V in NSP10 have been identified as the three most frequent variants and characterized using X-ray crystallography, biophysical assays, and enhanced sampling simulations. In contrast to other proteins such as spike and NSP6, NSP10 is significantly less prone to mutation due to its crucial role in replication. The functional effects of the variants were examined for their impact on the binding affinity and stability of both NSP14-NSP10 and NSP16-NSP10 complexes. These results highlight the limited changes induced by variant evolution in NSP10 and reflect on the critical roles NSP10 plays during the SARS-CoV-2 life cycle. These results also indicate that there is limited capacity for the virus to overcome inhibitors targeting NSP10 via the generation of variants in inhibitor binding pockets.