Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen-Deshalogen Matched Molecular Pairs.

In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of <1 kcal/mol despite the semi-automated calculation scheme. We quantify the accuracy of the optimized potential for liquid simulations (OPLS) force field to predict the effect of halogen addition to compounds, a commonly employed chemical modification in the design of drug-like molecules.

Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations.

Relative binding free energy (RBFE) prediction methods such as free energy perturbation (FEP) are important today for estimating protein-ligand binding affinities. Significant hardware and algorithmic improvements now allow for simulating congeneric series within days. Therefore, RBFE calculations have an enormous potential for structure-based drug discovery. As typically only a few representative crystal structures for a series are available, other ligands and design proposals must be reliably superimposed for meaningful results. An observed significant effect of the alignment on FEP led us to develop an alignment approach combining docking with maximum common substructure (MCS) derived core constraints from the most similar reference pose, named MCS-docking workflow. We then studied the effect of binding pose generation on the accuracy of RBFE predictions using six ligand series from five pharmaceutically relevant protein targets. Overall, the MCS-docking workflow generated consistent poses for most of the ligands in the investigated series. While multiple alignment methods often resulted in comparable FEP predictions, for most of the cases herein, the MCS-docking workflow produced the best accuracy in predictions. Furthermore, the FEP analysis data strongly support the hypothesis that the accuracy of RBFE predictions depends on input poses to construct the perturbation map. Therefore, an automated workflow without manual intervention minimizes potential errors and obtains the most useful predictions with significant impact for structure-based design.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Relative Binding Free Energy Calculations Applied to Protein Homology Models.

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Accurate Binding Free Energy Predictions in Fragment Optimization.

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling.

3-D ligand conformations are required for most ligand-based drug design methods, such as pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many studies of conformational search methods have focused on the reproduction of crystal structures (i.e. bioactive conformations); however, for ligand-based modeling the key question is how to generate a ligand alignment that produces the best results for a given query molecule. In this work, we study different conformation generation modes of ConfGen and the impact on virtual screening (Shape Screening and e-Pharmacophore) and QSAR predictions (atom-based and field-based). In addition, we develop a new search method, called common scaffold alignment, that automatically detects the maximum common scaffold between each screening molecule and the query to ensure identical coordinates of the common core, thereby minimizing the noise introduced by analogous parts of the molecules. In general, we find that virtual screening results are relatively insensitive to the conformational search protocol; hence, a conformational search method that generates fewer conformations could be considered "better" because it is more computationally efficient for screening. However, for 3-D QSAR modeling we find that more thorough conformational sampling tends to produce better QSAR predictions. In addition, significant improvements in QSAR predictions are obtained with the common scaffold alignment protocol developed in this work, which focuses conformational sampling on parts of the molecules that are not part of the common scaffold.

Probing the dynamic nature of water molecules and their influences on ligand binding in a model binding site.

The model binding site of the cytochrome c peroxidase (CCP) W191G mutant is used to investigate the structural and dynamic properties of the water network at the buried cavity using computational methods supported by crystallographic analysis. In particular, the differences of the hydration pattern between the uncomplexed state and various complexed forms are analyzed as well as the differences between five complexes of CCP W191G with structurally closely related ligands. The ability of docking programs to correctly handle the water molecules in these systems is studied in detail. It is found that fully automated prediction of water replacement or retention upon docking works well if some additional preselection is carried out but not necessarily if the entire water network in the cavity is used as input. On the other hand, molecular interaction fields for water calculated from static crystal structures and hydration density maps obtained from molecular dynamics simulations agree very well with crystallographically observed water positions. For one complex, the docking and MD results sensitively depend on the quality of the starting structure, and agreement is obtained only after redetermination of the crystal structure and refinement at higher resolution.

Small-molecule targeting of MUSASHI RNA-binding activity in acute myeloid leukemia.

The MUSASHI (MSI) family of RNA binding proteins (MSI1 and MSI2) contribute to a wide spectrum of cancers including acute myeloid leukemia. We find that the small molecule Ro 08-2750 (Ro) binds directly and selectively to MSI2 and competes for its RNA binding in biochemical assays. Ro treatment in mouse and human myeloid leukemia cells results in an increase in differentiation and apoptosis, inhibition of known MSI-targets, and a shared global gene expression signature similar to shRNA depletion of MSI2. Ro demonstrates in vivo inhibition of c-MYC and reduces disease burden in a murine AML leukemia model. Thus, we identify a small molecule that targets MSI's oncogenic activity. Our study provides a framework for targeting RNA binding proteins in cancer.

The Role of Bridging Water and Hydrogen Bonding as Key Determinants of Noncovalent Protein-Carbohydrate Recognition.

Mechanisms of protein-carbohydrate recognition attract a lot of interest due to their roles in various cellular processes and metabolism disorders. We have performed a large-scale analysis of protein structures solved in complex with glucose, galactose and their substituted analogues. We found that, on average, sugar molecules establish five hydrogen bonds (HBs) in the binding site, including one to three HBs with bridging water molecules. The free energy contribution of bridging and direct HBs was estimated using the free energy perturbation (FEP+) methodology for mono- and disaccharides that bind to l-ABP, ttGBP, TrmB, hGalectin-1 and hGalectin-3. We show that removing hydroxy groups that are engaged in direct HBs with the charged groups of Asp, Arg and Glu residues, protein backbone amide or buried water dramatically decreases binding affinity. In contrast, all solvent-exposed hydroxy groups and hydroxy groups engaged in HBs with the solvent-exposed bridging water molecules contribute weakly to binding affinity and so can be replaced to optimize ligand potency. Finally, we rationalize an effect of binding site water replacement on the binding affinity to l-ABP.

Calculating Water Thermodynamics in the Binding Site of Proteins - Applications of WaterMap to Drug Discovery.

The ability to accurately characterize the solvation properties (water locations and thermodynamics) of biomolecules is of great importance to drug discovery. While crystallography, NMR, and other experimental techniques can assist in determining the structure of water networks in proteins and protein-ligand complexes, most water molecules are not fully resolved and accurately placed. Furthermore, understanding the energetic effects of solvation and desolvation on binding requires an analysis of the thermodynamic properties of solvent involved in the interaction between ligands and proteins. WaterMap is a molecular dynamics-based computational method that uses statistical mechanics to describe the thermodynamic properties (entropy, enthalpy, and free energy) of water molecules at the surface of proteins. This method can be used to assess the solvent contributions to ligand binding affinity and to guide lead optimization. In this review, we provide a comprehensive summary of published uses of WaterMap, including applications to lead optimization, virtual screening, selectivity analysis, ligand pose prediction, and druggability assessment.

Optimization of potent DFG-in inhibitors of platelet derived growth factor receptorß (PDGF-Rß) guided by water thermodynamics.

In this study we report on the hit optimization of substituted 3,5-diaryl-pyrazin-2(1H)-ones toward potent and effective platelet-derived growth factor receptor (PDGF-R) ß-inhibitors. Originally, the 3,5-diaryl-pyrazin-2-one core was derived from the marine sponge alkaloid family of hamacanthins. In our first series compound 2 was discovered as a promising hit showing strong activity against PDGF-Rß in the kinase assay (IC50 = 0.5 µM). Furthermore, 2 was shown to be selective for PDGF-Rß in a panel of 24 therapeutically relevant protein kinases. Molecular modeling studies on a PDGF-Rß homology model using prediction of water thermodynamics suggested an optimization strategy for the 3,5-diaryl-pyrazin-2-ones as DFG-in binders by using a phenolic OH function to replace a structural water molecule in the ATP binding site. Indeed, we identified compound 38 as a highly potent inhibitor with an IC50 value of 0.02 µM in a PDGF-Rß enzymatic assay also showing activity against PDGF-R dependent cancer cells.

Molecular dynamics reveal binding mode of glutathionylspermidine by trypanothione synthetase.

The trypanothione synthetase (TryS) catalyses the two-step biosynthesis of trypanothione from spermidine and glutathione and is an attractive new drug target for the development of trypanocidal and antileishmanial drugs, especially since the structural information of TryS from Leishmania major has become available. Unfortunately, the TryS structure was solved without any of the substrates and lacks loop regions that are mechanistically important. This contribution describes docking and molecular dynamics simulations that led to further insights into trypanothione biosynthesis and, in particular, explains the binding modes of substrates for the second catalytic step. The structural model essentially confirm previously proposed binding sites for glutathione, ATP and two Mg(2+) ions, which appear identical for both catalytic steps. The analysis of an unsolved loop region near the proposed spermidine binding site revealed a new pocket that was demonstrated to bind glutathionylspermidine in an inverted orientation. For the second step of trypanothione synthesis glutathionylspermidine is bound in a way that preferentially allows N(1)-glutathionylation of N(8)-glutathionylspermidine, classifying N(8)-glutathionylspermidine as the favoured substrate. By inhibitor docking, the binding site for N(8)-glutathionylspermidine was characterised as druggable.

Novel Inverse Binding Mode of Indirubin Derivatives Yields Improved Selectivity for DYRK Kinases.

DYRK kinases are involved in alternative pre-mRNA splicing as well as in neuropathological states such as Alzheimer's disease and Down syndrome. In this study, we present the design, synthesis, and biological evaluation of indirubins as DYRK inhibitors with enhanced selectivity. Modifications of the bis-indole included polar or acidic functionalities at positions 5' and 6' and a bromine or a trifluoromethyl group at position 7, affording analogues that possess high activity and pronounced specificity. Compound 6i carrying a 5'-carboxylate moiety demonstrated the best inhibitory profile. A novel inverse binding mode, which forms the basis for the improved selectivity, was suggested by molecular modeling and confirmed by determining the crystal structure of DYRK2 in complex with 6i. Structure-activity relationships were further established, including a thermodynamic analysis of binding site water molecules, offering a structural explanation for the selective DYRK inhibition.