Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Tripartite efflux pumps of the RND superfamily: what did we learn from computational studies?

Bacterial resistance to antibiotics has been long recognized as a priority to address for human health. Among all micro-organisms, the so-called multi-drug resistant (MDR) bacteria, which are resistant to most, if not all drugs in our current arsenal, are particularly worrisome. The World Health Organization has prioritized the ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species) pathogens, which include four Gram-negative bacterial species. In these bacteria, active extrusion of antimicrobial compounds out of the cell by means of 'molecular guns' known as efflux pumps is a main determinant of MDR phenotypes. The resistance-nodulation-cell division (RND) superfamily of efflux pumps connecting the inner and outer membrane in Gram-negative bacteria is crucial to the onset of MDR and virulence, as well as biofilm formation. Thus, understanding the molecular basis of the interaction of antibiotics and inhibitors with these pumps is key to the design of more effective therapeutics. With the aim to contribute to this challenge, and complement and inspire experimental research, in silico studies on RND efflux pumps have flourished in recent decades. Here, we review a selection of such investigations addressing the main determinants behind the polyspecificity of these pumps, the mechanisms of substrate recognition, transport and inhibition, as well as the relevance of their assembly for proper functioning, and the role of protein-lipid interactions. The journey will end with a perspective on the role of computer simulations in addressing the challenges posed by these beautifully complex machineries and in supporting the fight against the spread of MDR bacteria.

Molecular rationale for the impairment of the MexAB-OprM efflux pump by a single mutation in MexA

Efflux pumps of the Resistance-Nodulation-cell Division (RND) superfamily contribute to intrinsic and acquired resistance in Gram-negative pathogens by expelling chemically unrelated antibiotics with high efficiency. They are tripartite systems constituted by an inner-membrane-anchored transporter, an outer membrane factor protein, and a membrane fusion protein. Multimerization of the membrane fusion protein is an essential prerequisite for full functionality of these efflux pumps. In this work, we employed complementary computational techniques to investigate the stability of a dimeric unit of MexA (the membrane fusion protein of the MexAB-OprM RND efflux pump of Pseudomonas aeruginosa), and to provide a molecular rationale for the effect of the G72S substitution, which affects MexAB-OprM functionality by impairing the assembly of MexA. Our findings indicate that: i) dimers of this protein are stable in multiple µs-long molecular dynamics simulations; ii) the mutation drastically alters the conformational equilibrium of MexA, favouring a collapsed conformation that is unlikely to form dimers or higher order assemblies. Unveiling the mechanistic aspects underlying large conformational distortions induced by minor sequence changes is informative to efforts at interfering with the activity of this elusive bacterial weapon. In this respect, our work further confirms how molecular simulations can give important contribution and useful insights to characterize the mechanism of highly complex biological systems.

Molecular Interactions of Cephalosporins with the Deep Binding Pocket of the RND Transporter AcrB

The drug/proton antiporter AcrB, part of the major efflux pump AcrABZ-TolC in Escherichia coli, is characterized by its impressive ability to transport chemically diverse compounds, conferring a multidrug resistance phenotype. However, the molecular features differentiating between good and poor substrates of the pump have yet to be identified. In this work, we combined molecular docking with molecular dynamics simulations to study the interactions between AcrB and two representative cephalosporins, cefepime and ceftazidime (a good and poor substrate of AcrB, respectively). Our analysis revealed different binding preferences of the two compounds toward the subsites of the large deep binding pocket of AcrB. Cefepime, although less hydrophobic than ceftazidime, showed a higher affinity than ceftazidime for the so-called hydrophobic trap, a region known for binding inhibitors and substrates. This supports the hypothesis that surface complementarity between the molecule and AcrB, more than the intrinsic hydrophobicity of the antibiotic, is a feature required for the interaction within this region. Oppositely, the preference of ceftazidime for binding outside the hydrophobic trap might not be optimal for triggering allosteric conformational changes needed to the transporter to accomplish its function. Altogether, our findings could provide valuable information for the design of new antibiotics less susceptible to the efflux mechanism.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

David operation: single center 10-year experience

AIM

Aortic valve-sparing operation has been progressively widely performed for the treatment of aortic root aneurysm. Nowadays, this procedure has been proposed even in presence of a bicuspid aortic valve, severe aortic regurgitation or in primary aortic dissection repair. We present our ten-year experience focusing on mid-term echocardiographic follow-up.

METHODS

Between June 2002 and February 2012, 139 patients (mean age of 61±12 years) underwent aortic valve-sparing operation with valve reimplantation. Twenty-seven patients (19%) had bicuspid aortic valve; in eighteen cases (13%) cusp motion or anatomical abnormalities concurred in determining aortic regurgitation and needed an adjunct cusp repair. A Gelweave Valsalva™ graft was implanted in all the patients.

RESULTS

The mortality pre-discharge was 0.7% (1 patient). The cumulative 1-year, 5-years and 8-years survival rates were 99%, 93% and 87% respectively. Postoperative aortic regurgitation more than mild degree (>2+/4+) was the only significant risk factors for redo aortic valve surgery Freedom from reoperation due to aortic valve regurgitation was 96% at 1 year, 90% at 5 years and 86% at 8 years. When comparing freedom from reoperation in patients with bicuspid vs tricuspid aortic valve, no differences were found (P=0.31) and the rate of aortic valve reoperation was significantly higher (P<0.001) in patients who received leaflet's repair.

CONCLUSION

The durability of valve reimplantation was found to be excellent in patients with tricuspid aortic valve and normal or nearly normal cusps. Cusp prolapse and complication after cusp repair turned out to be the main causes for early failure.