Optimizing fragment and scaffold docking by use of molecular interaction fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.     

Computer based screening of compound databases: 1. Preselection of benzamidine-based thrombin inhibitors

We present a computational protocol which uses the known three-dimensional structure of a target enzyme to identify possible ligands from databases of compounds with low molecular weight. This is accomplished by first mapping the essential interactions in the binding site with the program GRID. The resulting regions of favorable interaction between target and ligand are translated into a database query, and with UNITY a flexible 3D database search is performed. The feasibility of this approach is calibrated with thrombin as the target. Our results show that the resulting hit lists are enriched with thrombin inhibitors compared to the total database.     

A minimalist approach toward protein recognition by epitope transfer from functionally evolved beta-sheet surfaces

New approaches for identifying small molecules that specifically target protein surfaces as opposed to active site clefts are of much current interest. Toward this goal, we describe a three-step methodology: in step one, we target a protein of interest by directed evolution of a small beta-sheet scaffold; in step two, we identify residues on the scaffold that are implicated in binding; and in step three, we transfer the chemical information from the beta-sheet to a small molecule mimic. As a case study, we targeted the proteolytic enzyme thrombin, involved in blood coagulation, utilizing a library of beta-sheet epitopes displayed on phage that were previously selected for conservation of structure. We found that the thrombin-binding, beta-sheet displaying mini-proteins retained their structure and stability while inhibiting thrombin at low micromolar inhibition constants. A conserved dityrosine recognition motif separated by 9.2 A was found to be common among the mini-protein inhibitors and was further verified by alanine scanning. A molecule containing two tyrosine residues separated by a linker that matched the spacing on the beta-sheet scaffold inhibited thrombin, whereas a similar dityrosine molecule separated by a shorter 6 A linker could not. Moreover, kinetic analysis revealed that both the mini-protein as well as its minimalist mimic with only two functional residues exhibited noncompetitive inhibition of thrombin. Thus, this reductionist approach affords a simple methodology for transferring information from structured protein scaffolds to yield small molecule leads for targeting protein surfaces with novel mechanisms of action.     

Do benzodiazepines mimic reverse-turn structures?

The role of benzodiazepine derivatives (BZD) as a privileged scaffold that mimics beta-turn structures (Ripka et al. (1993) Tetrahedron 49:3593-3608) in peptide/protein recognition was reexamined in detail. Stable BZD ring conformers were determined with MM3, and experimental reverse-turn structures were extracted from the basis set of protein crystal structures previously defined by Ripka et al. Ideal beta-turns were also modeled and similarly compared with BZD conformers. Huge numbers of conformers were generated by systematically scanning the torsional degrees of freedom for BZDs, as well as those of ideal beta-turns for comparison. Using these structures, conformers of BZDs were fit to experimental structures as suggested by Ripka et al., or modeled classical beta-turn conformers, and the root-mean-square deviation (RMSD) values were calculated for each pairwise comparison. Pairs of conformers with the smallest RMSD values for overlap of the four alpha-beta side-chain orientations were selected. All overlaps of BZD conformers with experimental beta-turns yielded one or more comparisons where the least RMSD was significantly small, 0.48-0.86 angstroms, as previously suggested. Utilizing a different methodology, the overall conclusion that benzodiazepines could serve as reverse-turn mimetics of Ripka et al. is justified. The least RMSD values for the overlap of BZDs and modeled classical beta-turns were also less than 1 angstrom. When comparing BZDs with experimental or classical beta-turns, the set of experimental beta-turns selected by Ripka et al. fit the BZD scaffolds better than modeled classical beta-turns; however, all the experimental beta-turns did not fit a particular BZD scaffold better. A single BZD ring conformation, and/or chiral orientation, can mimic some, but not all, of the experimental beta-turn structures. BZD has two central ring conformations and one chiral center that explains why the four variations of the BZD scaffold can mimic all types of beta-turn structure examined. It was found, moreover, that the BZD scaffold also mimics each of the nine clusters of experimental orientations of side chains of reverse turns in the Protein Data Bank, when the new classification scheme for the four side-chain directions (the relative orientations of alpha-beta vectors of residues i through i+3) was considered (Tran et al. (2005) J Comput-Aided Mol Des 19:551-566).     

Synthesis of flavonoid analogues as scaffolds for natural product-based combinatorial libraries

The design and synthesis of flavonoid analogues as combinatorial scaffolds is reported. Using commercially available materials, we synthesized chalcones with fluoro and carboxy groups. Nitration of these compounds generated highly functionalized flavonoid scaffolds with an o-fluoronitrobenzene template. Subsequent cyclizations of these chalcones resulted in the formation of several flavone and flavonone scaffolds. One of the flavonones was chosen as the scaffold to synthesize flavonoid derivatives on the solid phase. A series of flavonoid derivatives were obtained in high yields, which demonstrates that these highly functionalized scaffolds can be used in the synthesis of natural product-based combinatorial libraries for drug discovery.     

An alignment‐free methodology for modelling field‐based 3D‐structure activity relationships using inductive logic programming

Traditional 3D‐quantitative structure–activity relationship (QSAR)/structure–activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine‐learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first‐order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd.     

Potent thrombin inhibitors via a 20-membered ring olefin metathesis macrocyclization

Twenty-membered ring pyrazinone derived macrocycles were prepared as a means to enhance the potency of existing thrombin inhibitors. Macrocyclization was accomplished via Grubbs olefin metathesis of a highly functionalized allyl-alloc scaffold, thus further confirming the power of such methodology.     

PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.     

Synthesis of novel bridged dinitrogen heterocycles and their evaluation as potential fragments for the design of biologically active compounds

A library of saturated bridged heterocycles based on 3,6-diazabicyclo[3.2.1]octane-2,4-dione and bispidine scaffolds (mean compound molecular weight is approximately 300 Da) with up to three stereocenters and four diversity points has been synthesized. Synthetic scaffold modifications leading to an increase in molecular complexity were studied. Well-defined stereochemical structures of both compound sets was confirmed by X-ray studies and halogenoaryl substituents were inserted appropriately for the design of novel non-basic serine protease inhibitors. Comprehensive molecular modeling has been performed for all synthesized compounds giving rationales of ligand–enzyme interactions with thrombin and trypsin. Biological testing confirmed moderate inhibitory activity of halogen-substituted saturated diazabicyclic small molecules towards thrombin.     

Performance validation of neural network based (13)c NMR prediction using a publicly available data source

The validation of the performance of a neural network based 13C NMR prediction algorithm using a test set available from an open source publicly available database, NMRShiftDB, is described. The validation was performed using a version of the database containing ca. 214,000 chemical shifts as well as for two subsets of the database to compare performance when overlap with the training set is taken into account. The first subset contained ca. 93,000 chemical shifts that were absent from the ACD\CNMR DB, the "excluded shift set" used for training of the neural network and the ACD\CNMR prediction algorithm, while the second contained ca. 121,000 shifts that were present in the ACD\CNMR DB training set, the "included shift set". This work has shown that the mean error between experimental and predicted shifts for the entire database is 1.59 ppm, while the mean deviation for the subset with included shifts is 1.47 and 1.74 ppm for excluded shifts. Since similar work has been reported online for another algorithm we compared the results with the errors determined using Robien's CNMR Neural Network Predictor using the entire NMRShiftDB for program validation.     

Optimization of fibrin gelation for enhanced cell seeding and proliferation in regenerative medicine applications

In order to improve the cell seeding efficiency and cell compatibility inside porous tissue scaffolds, a method of fibrin gel‐mediated cell encapsulation inside the scaffold was optimized. Disc‐type poly(d ,l ‐glycolic‐co‐lactic acid) (PLGA) scaffolds without a dense surface skin layer were fabricated using an established solvent casting and particulate leaching method as a model porous scaffold, which showed high porosity ranging from 90 ± 2% to 96 ± 2%. The thrombin and fibrinogen concentration as precursors of fibrin gel was varied to control the gelation kinetics as measured by rheology analysis, and optimized conditions were developed for a uniform fibrin gel formation with the target cells inside the porous PLGA scaffold. The fibroblast cell seeding accompanied by a uniform fibrin gel formation at an optimized gelation condition inside the PLGA scaffold resulted in an increase in cell seeding efficiency, a better cell proliferation, and an increase in final cell density inside the scaffold. Scanning electron microscopy images revealed that cells were better spread and grown by fibrin gel encapsulation inside scaffold compared with the case of bare PLGA scaffold. Copyright © 2016 John Wiley & Sons, Ltd.     

Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease

The ongoing coronavirus pandemic has been a burden on the worldwide population, with mass fatalities and devastating socioeconomic consequences. It has particularly drawn attention to the lack of approved small-molecule drugs to inhibit SARS coronaviruses. Importantly, lessons learned from the SARS outbreak of 2002–2004, caused by severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), can be applied to current drug discovery ventures. SARS-CoV-1 and SARS-CoV-2 both possess two cysteine proteases, the main protease (M^pro) and the papain-like protease (PL^pro), which play a significant role in facilitating viral replication, and are important drug targets. The non-covalent inhibitor, GRL-0617, which was found to inhibit replication of SARS-CoV-1, and more recently SARS-CoV-2, is the only PL^pro inhibitor co-crystallised with the recently solved SARS-CoV-2 PL^pro crystal structure. Therefore, the GRL-0617 structural template and pharmacophore features are instrumental in the design and development of more potent PL^pro inhibitors. In this work, we conducted scaffold hopping using GRL-0617 as a reference to screen over 339,000 ligands in the chemical space using the ChemDiv, MayBridge, and Enamine screening libraries. Twenty-four distinct scaffolds with structural and electrostatic similarity to GRL-0617 were obtained. These proceeded to molecular docking against PL^pro using the AutoDock tools. Of two compounds that showed the most favourable predicted binding affinities to the target site, as well as comparable protein-ligand interactions to GRL-0617, one was chosen for further analogue-based work. Twenty-seven analogues of this compound were further docked against the PL^pro, which resulted in two additional hits with promising docking profiles. Our in silico pipeline consisted of an integrative four-step approach: (1) ligand-based virtual screening (scaffold-hopping), (2) molecular docking, (3) an analogue search, and, (4) evaluation of scaffold drug-likeness, to identify promising scaffolds and eliminate those with undesirable properties. Overall, we present four novel, and lipophilic, scaffolds obtained from an exhaustive search of diverse and uncharted regions of chemical space, which may be further explored in vitro through structure-activity relationship (SAR) studies in the search for more potent inhibitors. Furthermore, these scaffolds were predicted to have fewer off-target interactions than GRL-0617. Lastly, to our knowledge, this work contains the largest ligand-based virtual screen performed against GRL-0617.     

Systematic assessment of scaffold distances in ChEMBL: prioritization of compound data sets for scaffold hopping analysis in virtual screening

The evaluation of the scaffold hopping potential of computational methods is of high relevance for virtual screening. For benchmark calculations, classes of known active compounds are utilized. Ideally, such classes should have a well-defined content of structurally diverse scaffolds. However, in reported benchmark investigations, the choice of activity classes is often difficult to rationalize. To provide a compendium of well-characterized test cases for the assessment of scaffold hopping potential, structural distances between scaffolds were systematically calculated for compound classes available in the ChEMBL database. Nearly seven million scaffold pairs were evaluated. On the basis of the global scaffold distance distribution, a threshold value for large scaffold distances was determined. Compound data sets were ranked based on the proportion of scaffold pairs with large distances they contained, taking additional criteria into account that are relevant for virtual screening. A set of 50 activity classes is provided that represent attractive test cases for scaffold hopping analysis and benchmark calculations.     

Development of a method to consistently quantify the structural distance between scaffolds and to assess scaffold hopping potential

We introduce a method to determine a structural distance between any pair of molecular scaffolds. The development of this approach was motivated by the need to accurately evaluate scaffold hopping studies in virtual screening and medicinal chemistry and assess the degree of difficulty involved in facilitating a transition from one structure to another. In order to consistently derive structural distances, scaffolds of different composition and topology are subjected to molecular editing procedures that abstract from original scaffolds in a defined manner until compositional and topological equivalence can be established. Pairs of corresponding scaffold representations are transformed into one-dimensional atom sequences that are aligned using approaches adapted from biological sequence comparison. From best scoring atom sequence alignments, interscaffold distances are derived. The algorithm is evaluated at different levels including the analysis of a series of model scaffolds with defined chemical changes, a scaffold library, and scaffolds from reference compounds and hits of successful virtual screening applications. It is demonstrated that chemically intuitive scaffold distances are obtained for pairs of scaffolds with varying composition and topology. Distance threshold values for close and remote structural relationships between scaffolds are also determined. The methodology is made publicly available in order to provide a basis for a consistent assessment of scaffold hopping ability and to aid in the evaluation and comparison of virtual screening methods.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction

For a systematic exploration of structural relationships between molecular scaffolds, ～24,000 unique scaffolds were extracted from 458 different target sets. Substructure relationships between these scaffolds were systematically determined. The scaffold tree data structure was utilized to study structural relationships between original scaffolds and derivative scaffolds obtained by rule-based decomposition. Leaf-to-root substructure relationships that resulted from rule-based decomposition were compared to leaf-to-leaf relationships between original scaffolds most of which were not part of the scaffold tree hierarchy. Decomposed scaffolds not contained in active target set compounds were prioritized on the basis of hierarchical scaffold patterns and additional substructure relationships. For high-priority virtual scaffolds, activity predictions were carried out, and these scaffolds were often found in external test compounds having the predicted activity. Taken together, our results suggest that leaf-to-root substructure relationships in scaffold trees should best be complemented with additional substructure relationships to determine high-priority virtual scaffolds for activity prediction.     

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.