Predicting binding events in very flexible, allosteric, multi-domain proteins.

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

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.

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4.

We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge (GC4) organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the 20 targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: (i) our EDES protocol (with minor modifications) was able to sample sub-ångstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~ 1 Å RMSD; (ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.

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.