The Impact of Variable Selection Coverage on Detection of Ligands from a DNA-Encoded Library Screen.

DNA-encoded library (DEL) technology has become a prominent screening platform in drug discovery owing to the capacity to screen billions or trillions of compounds in a single experiment. Although numerous successes with DEL technology have been reported, we are unaware of a rigorous examination of the many different variables that can influence a screen's success. Herein, we explore the impact of variable sample sequencing depth on the detection of tool compounds with known affinities toward a given target while simultaneously probing the effect of initial compound input. Our sequencing data confirm reports that high-affinity compounds can be discovered directly from a DEL screen, but we demonstrate that a mismatch between selection output and sequencing quantity can obscure useful ligands. Our results highlight the importance of selection coverage in grasping the entire picture of a DEL screen where the signal of a weak or underrepresented ligand may be suppressed by the inherent noise of a selection. These potential missed ligands may be critical to the success or failure of a drug discovery program.

Synthesis of Multifunctional 2-Aminobenzimidazoles on DNA via Iodine-Promoted Cyclization.

2-Aminobenzimidazole cores are among the most common structural components in medicinal chemistry and can be found in many biologically active molecules. Herein, we report a mild protocol for the synthesis of multifunctional 2-aminobenzimidazoles on-DNA with broad substrate scopes. The reaction conditions expand our ability to design and synthesize 2-aminobenzimidazole core-focused DNA-encoded libraries.

Discovery and Optimization of Quinolinone Derivatives as Potent, Selective, and Orally Bioavailable Mutant Isocitrate Dehydrogenase 1 (mIDH1) Inhibitors.

Mutations at the arginine residue (R132) in isocitrate dehydrogenase 1 (IDH1) are frequently identified in various human cancers. Inhibition of mutant IDH1 (mIDH1) with small molecules has been clinically validated as a promising therapeutic treatment for acute myeloid leukemia and multiple solid tumors. Herein, we report the discovery and optimization of a series of quinolinones to provide potent and orally bioavailable mIDH1 inhibitors with selectivity over wild-type IDH1. The X-ray structure of an early lead 24 in complex with mIDH1-R132H shows that the inhibitor unexpectedly binds to an allosteric site. Efforts to improve the in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) properties of 24 yielded a preclinical candidate 63. The detailed preclinical ADME and pharmacology studies of 63 support further development of quinolinone-based mIDH1 inhibitors as therapeutic agents in human trials.

Discovery and optimization of novel piperazines as potent inhibitors of fatty acid synthase (FASN).

The discovery, structure-activity relationships, and optimization of a novel class of fatty acid synthase (FASN) inhibitors is reported. High throughput screening identified a series of substituted piperazines with structural features that enable interactions with many of the potency-driving regions of the FASN KR domain binding site. Derived from this series was FT113, a compound with potent biochemical and cellular activity, which translated into excellent activity in in vivo models.

Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections.

DNA-encoded chemical libraries (DELs) provide a high-throughput and cost-effective route for screening billions of unique molecules for binding affinity for diverse protein targets. Identifying candidate compounds from these libraries involves affinity selection, DNA sequencing, and measuring enrichment in a sample pool of DNA barcodes. Successful detection of potent binders is affected by many factors, including selection parameters, chemical yields, library amplification, sequencing depth, sequencing errors, library sizes, and the chosen enrichment metric. To date, there has not been a clear consensus about how enrichment from DEL selections should be measured or reported. We propose a normalized  z-score enrichment metric using a binomial distribution model that satisfies important criteria that are relevant for analysis of DEL selection data. The introduced metric is robust with respect to library diversity and sampling and allows for quantitative comparisons of enrichment of n-synthons from parallel DEL selections. These features enable a comparative enrichment analysis strategy that can provide valuable information about hit compounds in early stage drug discovery.

Discovery of novel N-hydroxy-2-arylisoindoline-4-carboxamides as potent and selective inhibitors of HDAC11.

N-Hydroxy-2-arylisoindoline-4-carboxamides are potent and selective inhibitors of HDAC11. The discovery, synthesis, and structure activity relationships of this novel series of inhibitors are reported. An advanced analog (FT895) displays promising cellular activity and pharmacokinetic properties that make it a useful tool to study the biology of HDAC11 and its potential use as a therapeutic target for oncology and inflammation indications.

Molecular basis of USP7 inhibition by selective small-molecule inhibitors.

Ubiquitination controls the stability of most cellular proteins, and its deregulation contributes to human diseases including cancer. Deubiquitinases remove ubiquitin from proteins, and their inhibition can induce the degradation of selected proteins, potentially including otherwise 'undruggable' targets. For example, the inhibition of ubiquitin-specific protease 7 (USP7) results in the degradation of the oncogenic E3 ligase MDM2, and leads to re-activation of the tumour suppressor p53 in various cancers. Here we report that two compounds, FT671 and FT827, inhibit USP7 with high affinity and specificity in vitro and within human cells. Co-crystal structures reveal that both compounds target a dynamic pocket near the catalytic centre of the auto-inhibited apo form of USP7, which differs from other USP deubiquitinases. Consistent with USP7 target engagement in cells, FT671 destabilizes USP7 substrates including MDM2, increases levels of p53, and results in the transcription of p53 target genes, induction of the tumour suppressor p21, and inhibition of tumour growth in mice.

Chemically Induced Degradation of the Oncogenic Transcription Factor BCL6.

The transcription factor BCL6 is a known driver of oncogenesis in lymphoid malignancies, including diffuse large B cell lymphoma (DLBCL). Disruption of its interaction with transcriptional repressors interferes with the oncogenic effects of BCL6. We used a structure-based drug design to develop highly potent compounds that block this interaction. A subset of these inhibitors also causes rapid ubiquitylation and degradation of BCL6 in cells. These compounds display significantly stronger induction of expression of BCL6-repressed genes and anti-proliferative effects than compounds that merely inhibit co-repressor interactions. This work establishes the BTB domain as a highly druggable structure, paving the way for the use of other members of this protein family as drug targets. The magnitude of effects elicited by this class of BCL6-degrading compounds exceeds that of our equipotent non-degrading inhibitors, suggesting opportunities for the development of BCL6-based lymphoma therapeutics.

Identification of hot spots within druggable binding regions by computational solvent mapping of proteins.

Here we apply the computational solvent mapping (CS-Map) algorithm toward the in silico identification of hot spots, that is, regions of protein binding sites that are major contributors to the binding energy and, hence, are prime targets in drug design. The CS-Map algorithm, developed for binding site characterization, moves small organic functional groups around the protein surface and determines their most energetically favorable binding positions. The utility of CS-Map algorithm toward the prediction of hot spot regions in druggable binding pockets is illustrated by three test systems: (1) renin aspartic protease, (2) a set of previously characterized druggable proteins, and (3) E. coli ketopantoate reductase. In each of the three studies, existing literature was used to verify our results. Based on our analyses, we conclude that the information provided by CS-Map can contribute substantially to the identification of hot spots, a necessary predecessor of fragment-based drug discovery efforts.

Clustering of domains of functionally related enzymes in the interaction database PRECISE by the generation of primary sequence patterns.

The PRECISE database was developed by our laboratory to allow for the systematic study of the ligand interactions common to a set of functionally related enzymes, where an interaction site is defined broadly as any residue(s) that interact with a ligand. During the construction of PRECISE, enzyme chains are extracted from the protein data bank (PDB) and clustered according to functional homology as defined by the enzyme commission (EC) nomenclature system. A sequence representative is chosen from each cluster based on the criterion set forth by the non-redundant PDB set, and pair-wise alignments of each cluster member to the representative are performed. Atom-based residue-ligand interactions are calculated for each cluster member, and the summation of ligand interactions for all cluster members at each aligned position is determined. Although we were able to successfully align most clusters using a simple dynamic programming algorithm, several cluster created exhibited poor pair-wise alignments of each cluster member to its sequence representative. We hypothesized that the observed alignment problems were, in most cases, due to the incorrect separation and alignment of different domains in multi-domain proteins, a mistake that frequently causes error proliferation in functional annotation. Here we present the results of generating primary sequence patterns for each poorly aligned cluster in PRECISE to assess the extent to which multi-domain proteins that are incorrectly aligned contributes to poor pair-wise alignments of each cluster member to its representative. This requires the use of an iterative locally optimal pair-wise alignment algorithm to build a hierarchical similarity-based sequence pattern for a set of functionally related enzymes. Our results show that poor alignments in PRECISE are caused most frequently by the misalignment of multi-domain proteins, and that the generation of primary sequence patterns for the assignment of sequence family membership yields better alignments for the functionally related enzyme clusters in PRECISE than our original alignment algorithm.

PRECISE: a Database of Predicted and Consensus Interaction Sites in Enzymes.

PRECISE (Predicted and Consensus Interaction Sites in Enzymes) is a database of interactions between the amino acid residues of an enzyme and its ligands (substrate and transition state analogs, cofactors, inhibitors and products). It is available online at http://precise.bu.edu/. In the current version, all information on interactions is extracted from the enzyme-ligand complexes in the Protein Data Bank (PDB) by performing the following steps: (i) clustering homologous enzyme chains such that, in each cluster, the proteins have the same EC number and all sequences are similar; (ii) selecting a representative chain for each cluster; (iii) selecting ligand types; (iv) finding non-bonded interactions and hydrogen bonds; and (v) summing the interactions for all chains within the cluster. The output of the search is the color-coded sequence of the representative. The colors indicate the total number of interactions found at each amino acid position in all chains of the cluster. Clicking on a residue displays a detailed list of interactions for that residue. Optional filters allow restricting the output to selected chains in the cluster, to non-bonded or hydrogen bonding interactions, and to selected ligand types. The binding site information is essential for understanding and altering substrate specificity and for the design of enzyme inhibitors.