Miriam Sgobba

Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM‐PBSA and MM‐GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein‐ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy‐minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein‐ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM‐PBSA or MM‐GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves.

Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening

Binding estimation after refinement (BEAR) is a novel automated computational procedure suitable for correcting and overcoming limitations of docking procedures such as poor scoring function and the generation of unreasonable ligand conformations. BEAR makes use of molecular dynamics simulation followed by MM‐PBSA and MM‐GBSA binding free energy estimates as tools to refine and rescore the structures obtained from docking virtual screenings. As binding estimation after refinement relies on molecular dynamics, the entire procedure can be tailored to the needs of the end‐user in terms of computational time and the desired accuracy of the results. In a validation test, binding estimation after refinement and rescoring resulted in a significant enrichment of known ligands among top scoring compounds compared with the original docking results. Binding estimation after refinement has direct and straightforward application in virtual screening for correcting both false‐positive and false‐negative hits, and should facilitate more reliable selection of biologically active molecules from compound databases.

Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations

In the last decades, molecular docking has emerged as an increasingly useful tool in the modern drug discovery process, but it still needs to overcome many hurdles and limitations such as how to account for protein flexibility and poor scoring function performance. For this reason, it has been recognized that in many cases docking results need to be post-processed to achieve a significant agreement with experimental activities. In this study, we have evaluated the performance of MM-PBSA and MM-GBSA scoring functions, implemented in our post-docking procedure BEAR, in rescoring docking solutions. For the first time, the performance of this post-docking procedure has been evaluated on six different biological targets (namely estrogen receptor, thymidine kinase, factor Xa, adenosine deaminase, aldose reductase, and enoyl ACP reductase) by using i) both a single and a multiple protein conformation approach, and ii) two different software, namely AutoDock and LibDock. The assessment has been based on two of the most important criteria for the evaluation of docking methods, i.e., the ability of known ligands to enrich the top positions of a ranked database with respect to molecular decoys, and the consistency of the docking poses with crystallographic binding modes. We found that, in many cases, MM-PBSA and MM-GBSA are able to yield higher enrichment factors compared to those obtained with the docking scoring functions alone. However, for only a minority of the cases, the enrichment factors obtained by using multiple protein conformations were higher than those obtained by using only one protein conformation.

Structure-Based and in silico Design of Hsp90 Inhibitors

The molecular chaperone Hsp90 is responsible for activation and stabilization of several oncoproteins in cancer cells, and has emerged as an important target in cancer treatment because of this pivotal role. In recent years, interests have arisen around structure‐based design of small molecules aimed at inhibiting the chaperone activity of Hsp90. In this review, we illustrate the recent advances in structure‐based and in silico strategies aimed at discovering and optimizing Hsp90 inhibitors.

Structural Models and Binding Site Prediction of the C‐terminal Domain of Human Hsp90: A New Target for Anticancer Drugs

Heat shock protein 90 is a valuable target for anticancer drugs because of its role in the activation and stabilization of multiple oncogenic signalling proteins. While several compounds inhibit heat shock protein 90 by binding the N‐terminal domain, recent studies have proved that the C‐terminal domain is important for dimerization of the chaperone and contains an additional binding site for inhibitors. Heat shock protein 90 inhibition achieved with molecules binding to the C‐terminal domain provides an additional and novel opportunity to design and develop drugs. Therefore, for the first time, we have investigated the structure and the dynamic behaviour of the C‐terminal domain of human heat shock protein 90 with and without the small‐middle domain, using homology modelling and molecular dynamics simulations. In addition, secondary structure predictions and peptide folding simulations proved useful to investigate a putative additional α‐helix located between H18 and β20 of the C‐terminal domain. Finally, we used the structural information to infer the location of the binding site located in the C‐terminal domain by using a number of computational tools. The predicted pocket is formed by two grooves located between helix H18, the loop downstream of H18 and the loop connecting helices H20 and H21 of each monomer of the C‐terminal domain, with only two amino acids contributing from each middle domain.

Exploring the binding site of C-terminal hsp90 inhibitors.

The 90 kDa heat shock protein (Hsp90) is a prominent target for anticancer drug discovery. While its N-terminal domain has been widely exploited, several lines of evidence are emerging in favor of targeting its C-terminal domain to conceive innovative drugs based on perturbation of the dimer interface. Here, we describe the application of several computational approaches useful to predict the location of the C-terminal binding site.

A computational workflow for the design of irreversible inhibitors of protein kinases.

Design of irreversible inhibitors is an emerging and relatively less explored strategy for the design of protein kinase inhibitors. In this paper, we present a computational workflow that was specifically conceived to assist such design. The workflow takes the form of a multi-step procedure that includes: the creation of a database of already known reversible inhibitors of protein kinases, the selection of the most promising scaffolds that bind one or more desired kinase templates, the modification of the scaffolds by introduction of chemically reactive groups (suitable cysteine traps) and the final evaluation of the reversible and irreversible protein–ligand complexes with molecular dynamics simulations and binding free energy predictions. Most of these steps were automated. In order to prove that this is viable, the workflow was tested on a database of known inhibitors of ERK2, a protein kinase possessing a cysteine in the ATP site. The modeled ERK2-ligand complexes and the values of the estimated binding free energies of the putative ligands provide useful indicators of their aptitude to bind reversibly and irreversibly to the protein kinase. Moreover, the computational data are used to rank the ligands according to their computed binding free energies and their ability to bind specific protein residues in the reversible and irreversible complexes, thereby providing a useful decision-making tool for each step of the design. In this work we present the overall procedure and the first proof of concept results.

In vitro effects of Plasmodium falciparum dihydrofolate reductase inhibitors on normal and cancer cell proliferation.

Two of the most important problems that have seriously compromised the utility of commonly used antimalarials are drug toxicity and drug resistance of malarial parasites, in particular for Plasmodium falciparum. Malaria treatment requires a long-term therapy which, besides inducing resistant plasmodium strains, is characterised by nonselective toxicity towards human cells. Drugs such as quinoline derivatives are characterised by a mechanism of action than can result both in DNA damage and in oxidative stress for human cells. All these effects are considered very important steps of carcinogenesis. In addition, recent studies showed that malaria and cancer may be correlated, and that antimalarial drugs pyrimethamine and chloroquine can act as promoting agents on the growth of MCF-7 cancer cells. In particular, previous in vitro and in vivo studies performed in our laboratory confirmed that whereas certain antimalarials are able to induce a significant slowing of tumour progression, others act as tumour promoters. These observations raise the possibility that antimalarial therapy may induce tumour progression, and further highlight that effective and safer compounds are surely needed. For these reasons, the selectivity of action and a possible interference with tumour cell proliferation are two important aspects that need to be evaluated. In a previous work, a molecular docking strategy followed by structural refinement of the protein–ligand complexes led to the identification of new inhibitors of the P. falciparum bifunctional dihydrofolate reductase-thymidylate synthase (DHFR-TS) enzyme, a well-characterised target of antimalarials. Further studies highlighted quantitative structure–activity relationships by pharmacophore analyses of classical and nonclassical Pf DHFR inhibitors. These novel inhibitors, belonging to the urea, thiourea, dihydrazine, and N-hydroxyamidine classes of compounds, have structures completely unrelated to classical antifolates and importantly, they inhibit both Pf DHFR and its highly resistant mutants with micromolar and submicromolar affinities. Therefore, they constitute interesting candidates for further evaluation and optimisation. Based on these premises, we have undertaken validation studies of these novel inhibitors by evaluation of their cytotoxicity and their potential effects on in vitro cancer cell growth. In this study, we test and validate two new inhibitors chosen on the basis of their activity profile and solubility, one belonging to the N-hydroxyamidine and the other to the thiourea classes (molecules 1b and 6g in Figure 1) and, for comparison, pyrimethamine, chloroquine, mepacrine, and prima-