In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule’s property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)—an analog of ligand efficiency (LE) for a group in the molecule—using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.     

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.     

Design and characterization of libraries of molecular fragments for use in NMR screening against protein targets

We have designed four generations of a low molecular weight fragment library for use in NMR-based screening against protein targets. The library initially contained 723 fragments which were selected manually from the Available Chemicals Directory. A series of in silico filters and property calculations were developed to automate the selection process, allowing a larger database of 1.79 M available compounds to be searched for a further 357 compounds that were added to the library. A kinase binding pharmacophore was then derived to select 174 kinase-focused fragments. Finally, an additional 61 fragments were selected to increase the number of different pharmacophores represented within the library. All of the fragments added to the library passed quality checks to ensure they were suitable for the screening protocol, with appropriate solubility, purity, chemical stability, and unambiguous NMR spectrum. The successive generations of libraries have been characterized through analysis of structural properties (molecular weight, lipophilicity, polar surface area, number of rotatable bonds, and hydrogen-bonding potential) and by analyzing their pharmacophoric complexity. These calculations have been used to compare the fragment libraries with a drug-like reference set of compounds and a set of molecules that bind to protein active sites. In addition, an analysis of the overall results of screening the library against the ATP binding site of two protein targets (HSP90 and CDK2) reveals different patterns of fragment binding, demonstrating that the approach can find selective compounds that discriminate between related binding sites.     

Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery

Fragment-based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high-affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.     

Structure-activity relationships in purine-based inhibitor binding to HSP90 isoforms.

Inhibition of the ATPase activity of the chaperone protein HSP90 is a potential strategy for treatment of cancers. We have determined structures of the HSP90alpha N-terminal domain complexed with the purine-based inhibitor, PU3, and analogs with enhanced potency both in enzyme and cell-based assays. The compounds induce upregulation of HSP70 and downregulation of the known HSP90 client proteins Raf-1, CDK4, and ErbB2, confirming that the molecules inhibit cell growth by a mechanism dependent on HSP90 inhibition. We have also determined the first structure of the N-terminal domain of HSP90beta, complexed with PU3. The structures allow a detailed rationale to be developed for the observed affinity of the PU3 class of compounds for HSP90 and also provide a structural framework for design of compounds with improved binding affinity and drug-like properties.     

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.     

Exploring Multi-Subsite Binding Pockets in Proteins: DEEP-STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

Ligand-based NMR techniques to study protein–ligand interactions are potent tools in drug design. Saturation transfer difference (STD) NMR spectroscopy stands out as one of the most versatile techniques, allowing screening of fragments libraries and providing structural information on binding modes. Recently, it has been shown that a multi-frequency STD NMR approach, differential epitope mapping (DEEP)-STD NMR, can provide additional information on the orientation of small ligands within the binding pocket. Here, the approach is extended to a so-called DEEP-STD NMR fingerprinting technique to explore the binding subsites of cholera toxin subunit B (CTB). To that aim, the synthesis of a set of new ligands is presented, which have been subject to a thorough study of their interactions with CTB by weak affinity chromatography (WAC) and NMR spectroscopy. Remarkably, the combination of DEEP-STD NMR fingerprinting and Hamiltonian replica exchange molecular dynamics has proved to be an excellent approach to explore the geometry, flexibility, and ligand occupancy of multi-subsite binding pockets. In the particular case of CTB, it allowed the existence of a hitherto unknown binding subsite adjacent to the GM1 binding pocket to be revealed, paving the way to the design of novel leads for inhibition of this relevant toxin.     

Molecular Simulation of HER2/neu Degradation by Inhibiting HSP90

Several flavonoids were investigated for the degradation of the HER2/neu (ErbB‐2), while the mechanism is still unknown. A hypothesis was generated here that the inhibition of HER2/neu expression was blocked by heat shock protein 90α (HSP90α) through 1‐benzyl‐3‐(5‐hydroxymethyl‐2‐furyl)indazole (YC‐1) derivatives and flavonoids. In order to ensure the accuracy of the simulated protein structure, the RMSD value between the ligand in crystal structure from PDB and the ligand docking into HSP90α was 1.13 Å. By molecular simulation, the flavonoids and YC‐1 derivatives were employed to dock into HSP90α. The results showed a good correlation between the evaluation scores of the flavonoids/HSP90α complexes and the IC₅₀ of flavonoids‐induced degradation of HER2/neu. The YC‐1 derivatives showed higher score values and lower interaction energies on average. Especially, the CLC107 got the highest rank in Potential of Mean Force (PMF) and Dock Score. Luteolin showed the highest dock score and quercetin had the lowest interaction energy of all flavonoid derivatives. This study investigated that the YC‐1 derivatives and the flavonoids may be potent inhibitors for HSP90α in antitumor strategies.     

A Combination of Spin Diffusion Methods for the Determination of Protein–Ligand Complex Structural Ensembles

Structure‐based drug design (SBDD) is a powerful and widely used approach to optimize affinity of drug candidates. With the recently introduced INPHARMA method, the binding mode of small molecules to their protein target can be characterized even if no spectroscopic information about the protein is known. Here, we show that the combination of the spin‐diffusion‐based NMR methods INPHARMA, trNOE, and STD results in an accurate scoring function for docking modes and therefore determination of protein–ligand complex structures. Applications are shown on the model system protein kinase A and the drug targets glycogen phosphorylase and soluble epoxide hydrolase (sEH). Multiplexing of several ligands improves the reliability of the scoring function further. The new score allows in the case of sEH detecting two binding modes of the ligand in its binding site, which was corroborated by X‐ray analysis.     

Novel strategy for three-dimensional fragment-based lead discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.     

A proteomics method using immunoaffinity fluorogenic derivatization–liquid chromatography/tandem mass spectrometry (FD‐LC‐MS/MS) to identify a set of interacting proteins

Biological functions in organisms are usually controlled by a set of interacting proteins, and identifying the proteins that interact is useful for understanding the mechanism of the functions. Immunoprecipitation is a method that utilizes the affinity of an antibody to isolate and identify the proteins that have interacted in a biological sample. In this study, the FD‐LC–MS/MS method, which involves fluorogenic derivatization followed by separation and quantification by HPLC and finally identification of proteins by HPLC–tandem mass spectrometry, was used to identify proteins in immunoprecipitated samples, using heat shock protein 90 (HSP90) as a model of an interacting protein in HepaRG cells. As a result, HSC70 protein, which was known to form a complex with HSP90, was isolated, together with three different types of HSP90‐beta. The results demonstrated that the proposed immunoaffinity–FD‐LC–MS/MS method could be useful for simultaneously detecting and identifying the proteins that interact with a certain protein.     

Structural and energetic insight into the interactions between the benzolactam inhibitors and tumor marker HSP90α

The heat shock protein 90α (HSP90α) provides a promising molecular target for cancer therapy. A series of novel benzolactam inhibitors exhibited distinct inhibitory activity for HSP90α. However, the structural basis for the impact of distinct R₁ substituent groups of nine benzolactam inhibitors on HSP90α binding affinities remains unknown. In this study, we carried out molecular docking, molecular dynamics (MD) simulations, and molecular mechanics and generalized Born/surface area (MM–GBSA) binding free energy calculations to address the differences. Molecular docking studies indicated that all nine compounds presented one conformation in the ATP-binding site of HSP90α N-terminal domain. MD simulations and subsequent MM–GBSA calculations revealed that the hydrophobic interactions between all compounds and HSP90α contributed the most to the binding affinity and a good linear correlation was obtained between the calculated and the experimental binding free energies (R = 0.88). The per residue decomposition revealed that the most remarkable differences of residue contributions were found in the residues Ala55, Ile96, and Leu107 defining a hydrophobic pocket for the R₁ group, consistent with the analysis of binding modes. This study may be helpful for the future design of novel HSP90α inhibitors.     

Ligand‐Induced Conformational Changes in HSP90 Monitored Time Resolved and Label Free—Towards a Conformational Activity Screening for Drug Discovery

Investigation of protein–ligand interactions is crucial during early drug‐discovery processes. ATR‐FTIR spectroscopy can detect label‐free protein–ligand interactions with high spatiotemporal resolution. Here we immobilized, as an example, the heat shock protein HSP90 on an ATR crystal. This protein is an important molecular target for drugs against several diseases including cancer. With our novel approach we investigated a ligand‐induced secondary structural change. Two specific binding modes of 19 drug‐like compounds were analyzed. Different binding modes can lead to different efficacy and specificity of different drugs. In addition, the k_obs values of ligand dissociation were obtained. The results were validated by X‐ray crystallography for the structural change and by SPR experiments for the dissociation kinetics, but our method yields all data in a single and simple experiment.     

Two‐dimensional replica‐exchange method for predicting protein–ligand binding structures

We have developed a two‐dimensional replica‐exchange method for the prediction of protein–ligand binding structures. The first dimension is the umbrella sampling along the reaction coordinate, which is the distance between a protein binding pocket and a ligand. The second dimension is the solute tempering, in which the interaction between a ligand and a protein and water is weakened. The second dimension is introduced to make a ligand follow the umbrella potential more easily and enhance the binding events, which should improve the sampling efficiency. As test cases, we applied our method to two protein‐ligand complex systems (MDM2 and HSP 90‐alpha). Starting from the configuration in which the protein and the ligand are far away from each other in each system, our method predicted the ligand binding structures in excellent agreement with the experimental data from Protein Data Bank much faster with the improved sampling efficiency than the replica‐exchange umbrella sampling method that we have previously developed. © 2013 Wiley Periodicals, Inc.     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery

Abnormal activity of cyclin-dependent kinase 8 (CDK8) along with its partner protein cyclin C (CycC) is a common feature of many diseases including colorectal cancer. Using molecular dynamics (MD) simulations, this study determined the dynamics of the CDK8-CycC system and we obtained detailed breakdowns of binding energy contributions for four type-I and five type-II CDK8 inhibitors. We revealed system motions and conformational changes that will affect ligand binding, confirmed the essentialness of CycC for inclusion in future computational studies, and provide guidance in development of CDK8 binders. We employed unbiased all-atom MD simulations for 500 ns on twelve CDK8-CycC systems, including apoproteins and protein–ligand complexes, then performed principal component analysis (PCA) and measured the RMSF of key regions to identify protein dynamics. Binding pocket volume analysis identified conformational changes that accompany ligand binding. Next, H-bond analysis, residue-wise interaction calculations, and MM/PBSA were performed to characterize protein–ligand interactions and find the binding energy. We discovered that CycC is vital for maintaining a proper conformation of CDK8 to facilitate ligand binding and that the system exhibits motion that should be carefully considered in future computational work. Surprisingly, we found that motion of the activation loop did not affect ligand binding. Type-I and type-II ligand binding is driven by van der Waals interactions, but electrostatic energy and entropic penalties affect type-II binding as well. Binding of both ligand types affects protein flexibility. Based on this we provide suggestions for development of tighter-binding CDK8 inhibitors and offer insight that can aid future computational studies.     

Combinatorial ligand design targeted at protein families

We describe a method to create ligands specific for a given protein family. The method is applied to generate ligand candidates for the cyclin-dependent kinase (CDK) family. The CDK family of proteins is involved in regulating the cell cycle by alternately activating and deactivating the cell's progression through the cycle. CDKs are activated by association with cyclin and are inhibited by complexation with small molecules. X-ray crystal structures are available for three of the thirteen known CDK family members: CDK2, CDK5 and CDK 6. In this work, we use novel computational approaches to design ligand candidates that are potentially inhibitory across the three CDK family members as well as more specific molecules which can potentially inhibit one or any combination of two of the three CDK family members. We define a new scoring term, SpecScore, to quantify the potential inhibitory power of the generated structures. According to a search of the World Drug Alerts, the highest scoring SpecScore molecule that is specific for the three CDK family members shows very similar chemical characteristics and functional groups to numerous molecules known to deactivate several members of the CDK family.     

Vertebrate collagenase inhibitor. II. Tetrapeptidyl hydroxamic acids.

To develop a potent and specific collagenase inhibitor, a series of tetrapeptidyl hydroxamic acids were synthesized, based on the previous findings with tripeptidyl derivatives (Chem. Pharm. Bull., 38, 1007-1011, 1990). Among the series of tetrapeptidyl derivatives synthesized, R-Gly-Pro-Leu-Ala-NHOH and R-Gly-Pro-D-Leu-D-Ala-NHOH were found to be highly specific and potent inhibitors against vertebrate collagenase with an IC50 of 10(-6) M order, where R stands for Boc or acyl group. Analysis of their structure-activity relationships showed a characteristic feature of the substrate-binding site of collagenase as follows: 1) the S1 subsite forms a shallow hydrophobic pocket, although glycine residue corresponds to the subsite of the natural collagen substrate: 2) the S2 subsite constitutes a bulky pocket with less requirement for hydrophobicity: 3) the S3 subsite preferentially accommodates Pro residue: and 4) the accommodation of the P4-P1 subsites of peptidyl collagenase inhibitor to the S4-S1 subsites is required to form a tight binding of its hydroxamic acid moiety to the zinc ion at the catalytic site of the enzyme. The introduction of an enantiometric dipeptide unit, D-Leu-D-Ala, to the P2-P1 subsites demonstrated an increased binding capacity to the extended S4-S1 subsites of collagenase, thus providing proteinase-resistant inhibitor.     

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking (“close”), (b) methods that align or dock to a single receptor (“cross”), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor (“min-cross”). Pose prediction using our “close” models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our “cross” methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, “close” methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.     

Stafia-1: a STAT5a-Selective Inhibitor Developed via Docking-Based Screening of in Silico O-Phosphorylated Fragments

We present a new approach for the identification of inhibitors of phosphorylation-dependent protein–protein interaction domains, in which phenolic fragments are adapted by in silico O-phosphorylation before docking-based screening. From a database of 10 369 180 compounds, we identified 85 021 natural product-derived phenolic fragments, which were virtually O-phosphorylated and screened for in silico binding to the STAT3 SH2 domain. Nine screening hits were then synthesized, eight of which showed a degree of in vitro inhibition of STAT3. After analysis of its selectivity profile, the most potent inhibitor was then developed to Stafia-1, the first small molecule shown to preferentially inhibit the STAT family member STAT5a over the close homologue STAT5b. A phosphonate prodrug based on Stafia-1 inhibited STAT5a with selectivity over STAT5b in human leukemia cells, providing the first demonstration of selective in vitro and intracellular inhibition of STAT5a by a small-molecule inhibitor.