Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.

We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and nonbinders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E) and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.

Proteome-wide covalent ligand discovery in native biological systems.

Small molecules are powerful tools for investigating protein function and can serve as leads for new therapeutics. Most human proteins, however, lack small-molecule ligands, and entire protein classes are considered 'undruggable'. Fragment-based ligand discovery can identify small-molecule probes for proteins that have proven difficult to target using high-throughput screening of complex compound libraries. Although reversibly binding ligands are commonly pursued, covalent fragments provide an alternative route to small-molecule probes, including those that can access regions of proteins that are difficult to target through binding affinity alone. Here we report a quantitative analysis of cysteine-reactive small-molecule fragments screened against thousands of proteins in human proteomes and cells. Covalent ligands were identified for >700 cysteines found in both druggable proteins and proteins deficient in chemical probes, including transcription factors, adaptor/scaffolding proteins, and uncharacterized proteins. Among the atypical ligand-protein interactions discovered were compounds that react preferentially with pro- (inactive) caspases. We used these ligands to distinguish extrinsic apoptosis pathways in human cell lines versus primary human T cells, showing that the former is largely mediated by caspase-8 while the latter depends on both caspase-8 and -10. Fragment-based covalent ligand discovery provides a greatly expanded portrait of the ligandable proteome and furnishes compounds that can illuminate protein functions in native biological systems.

Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA.

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 µM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand-receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein-ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.

Crystallographic fragment-based drug discovery: use of a brominated fragment library targeting HIV protease.

A library of 68 brominated fragments was screened against a new crystal form of inhibited HIV-1 protease in order to probe surface sites in soaking experiments. Often, fragments are weak binders with partial occupancy, resulting in weak, difficult-to-fit electron density. The use of a brominated fragment library addresses this challenge, as bromine can be located unequivocally via anomalous scattering. Data collection was carried out in an automated fashion using AutoDrug at SSRL. Novel hits were identified in the known surface sites: 3-bromo-2,6-dimethoxybenzoic acid (Br6) in the flap site and 1-bromo-2-naphthoic acid (Br27) in the exosite, expanding the chemistry of known fragments for development of higher affinity potential allosteric inhibitors. At the same time, mapping the binding sites of a number of weaker binding Br-fragments provides further insight into the nature of these surface pockets.

N-linked peptidoresorc[4]arene-based receptors as noncompetitive inhibitors for α-chymotrypsin.

This paper deals with the design, synthesis, and evaluation of a new series of receptors for protein surface recognition. The design of these agents is based around the attachment of four constrained dipeptide chains onto a central resorc[4]arene scaffold. By varying the sequence, nature, and stereochemistry of the chains we prepared anionically functionalized N-linked peptidoresorc[4]arenes 12, 13, and 17 by Pd/C-catalyzed hydrogenation of the corresponding benzyl esters 10, 11, and 16. From this family of receptors we have identified noncompetitive inhibitors of α-chymotrypsin (ChT), which function by binding to the surface of the enzyme in the neighborhood of the active site cleft (K(i) values ranging from 12.4 ± 5.1 µM for free carboxylic acid (+)-12b to 0.76 ± 0.14 µM for benzyl ester (-)-16a). For anionically functionalized receptors 12, 13, and 17 the ChT inhibition is based essentially on electrostatic interaction, and the bound enzyme can be released from the resorcarene surface by increasing the ionic strength, with its activity almost completely restored. For receptors with terminal benzyl ester groups (10 and 16) a hydrophobic network can be suggested.

Synthesis, in vitro, and in vivo biological evaluation and molecular docking simulations of chiral alcohol and ether derivatives of the 1,5-diarylpyrrole scaffold as novel anti-inflammatory and analgesic agents.

Following our previous research on anti-inflammatory drugs (NSAIDs), we report here the synthesis of chiral 1,5-diarylpyrroles derivatives that were characterized for their in vitro inhibitory effects toward cyclooxygenase (COX) isozymes. Analysis of enzymatic affinity and COX-2 selectivity led us to the selection of one compound (+/-)-10b that was further tested in vitro in the human whole blood (HWB) and in vivo for its anti-inflammatory activity in mice. The affinity data have been rationalized through docking simulations.