Publications by authors named "Peter Kutchukian"

27 Publications

  • Page 1 of 1

Discovery of an Anion-Dependent Farnesyltransferase Inhibitor from a Phenotypic Screen.

ACS Med Chem Lett 2021 Jan 23;12(1):99-106. Epub 2020 Dec 23.

MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States.

By employing a phenotypic screen, a set of compounds, exemplified by , were identified which potentiate the ability of histone deacetylase inhibitor vorinostat to reverse HIV latency. Proteome enrichment followed by quantitative mass spectrometric analysis employing a modified analogue of as affinity bait identified farnesyl transferase (FTase) as the primary interacting protein in cell lysates. This ligand-FTase binding interaction was confirmed via X-ray crystallography and temperature dependent fluorescence studies, despite lacking structural and binding similarity to known FTase inhibitors. Although multiple lines of evidence established the binding interaction, these ligands exhibited minimal inhibitory activity in a cell-free biochemical FTase inhibition assay. Subsequent modification of the biochemical assay by increasing anion concentration demonstrated FTase inhibitory activity in this novel class. We propose binds together with the anion in the active site to inhibit farnesyl transferase. Implications for phenotypic screening deconvolution and HIV reactivation are discussed.
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http://dx.doi.org/10.1021/acsmedchemlett.0c00551DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812668PMC
January 2021

Molecular Profiling Reveals a Common Metabolic Signature of Tissue Fibrosis.

Cell Rep Med 2020 Jul 21;1(4):100056. Epub 2020 Jul 21.

Department of Cardiometabolic Diseases, MRL, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, USA.

Fibrosis, or the accumulation of extracellular matrix, is a common feature of many chronic diseases. To interrogate core molecular pathways underlying fibrosis, we cross-examine human primary cells from various tissues treated with TGF-β, as well as kidney and liver fibrosis models. Transcriptome analyses reveal that genes involved in fatty acid oxidation are significantly perturbed. Furthermore, mitochondrial dysfunction and acylcarnitine accumulation are found in fibrotic tissues. Substantial downregulation of the PGC1α gene is evident in both and fibrosis models, suggesting a common node of metabolic signature for tissue fibrosis. In order to identify suppressors of fibrosis, we carry out a compound library phenotypic screen and identify AMPK and PPAR as highly enriched targets. We further show that pharmacological treatment of MK-8722 (AMPK activator) and MK-4074 (ACC inhibitor) reduce fibrosis . Altogether, our work demonstrate that metabolic defect is integral to TGF-β signaling and fibrosis.
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http://dx.doi.org/10.1016/j.xcrm.2020.100056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659620PMC
July 2020

Cyp1 Inhibition Prevents Doxorubicin-Induced Cardiomyopathy in a Zebrafish Heart-Failure Model.

Chembiochem 2020 07 6;21(13):1905-1910. Epub 2020 Mar 6.

Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84112, USA.

Doxorubicin is a highly effective chemotherapy agent used to treat many common malignancies. However, its use is limited by cardiotoxicity, and cumulative doses exponentially increase the risk of heart failure. To identify novel heart failure treatment targets, a zebrafish model of doxorubicin-induced cardiomyopathy was previously established for small-molecule screening. Using this model, several small molecules that prevent doxorubicin-induced cardiotoxicity both in zebrafish and in mouse models have previously been identified. In this study, exploration of doxorubicin cardiotoxicity is expanded by screening 2271 small molecules from a proprietary, target-annotated tool compound collection. It is found that 120 small molecules can prevent doxorubicin-induced cardiotoxicity, including 7 highly effective compounds. Of these, all seven exhibited inhibitory activity towards cytochrome P450 family 1 (CYP1). These results are consistent with previous findings, in which visnagin, a CYP1 inhibitor, also prevents doxorubicin-induced cardiotoxicity. Importantly, genetic mutation of cyp1a protected zebrafish against doxorubicin-induced cardiotoxicity phenotypes. Together, these results provide strong evidence that CYP1 is an important contributor to doxorubicin-induced cardiotoxicity and highlight the CYP1 pathway as a candidate therapeutic target for clinical cardioprotection.
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http://dx.doi.org/10.1002/cbic.201900741DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500981PMC
July 2020

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space.

SLAS Discov 2020 04 8;25(4):384-396. Epub 2019 Nov 8.

Merck & Co., Inc., Boston, MA, USA.

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA-small molecule interactions that was generated was compared with that for protein-small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.
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http://dx.doi.org/10.1177/2472555219885373DOI Listing
April 2020

CHEMGENIE: integration of chemogenomics data for applications in chemical biology.

Drug Discov Today 2018 01 14;23(1):151-160. Epub 2017 Sep 14.

Modeling and Informatics, Merck & Co., Inc., Boston, MA, USA. Electronic address:

Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.
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http://dx.doi.org/10.1016/j.drudis.2017.09.004DOI Listing
January 2018

Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.

ACS Chem Biol 2017 09 29;12(9):2448-2456. Epub 2017 Aug 29.

Modeling & Informatics, Merck Research Laboratories , Boston, Massachusetts, United States.

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.
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http://dx.doi.org/10.1021/acschembio.7b00468DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298432PMC
September 2017

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

SLAS Discov 2017 Sep 20;22(8):995-1006. Epub 2017 Apr 20.

1 Modeling and Informatics, Merck & Co., Inc., West Point, PA, USA.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.
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http://dx.doi.org/10.1177/2472555217706058DOI Listing
September 2017

Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

PLoS Comput Biol 2017 02 9;13(2):e1005335. Epub 2017 Feb 9.

Informatics, Merck Research Laboratories, West Point, Pennsylvania, United States of America.

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.
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http://dx.doi.org/10.1371/journal.pcbi.1005335DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300121PMC
February 2017

Iterative Focused Screening with Biological Fingerprints Identifies Selective Asc-1 Inhibitors Distinct from Traditional High Throughput Screening.

ACS Chem Biol 2017 02 6;12(2):519-527. Epub 2017 Jan 6.

Screening and Protein Sciences, Merck & Co., Inc., MRL , North Wales, Pennsylvania, United States.

N-methyl-d-aspartate receptors (NMDARs) mediate glutamatergic signaling that is critical to cognitive processes in the central nervous system, and NMDAR hypofunction is thought to contribute to cognitive impairment observed in both schizophrenia and Alzheimer's disease. One approach to enhance the function of NMDAR is to increase the concentration of an NMDAR coagonist, such as glycine or d-serine, in the synaptic cleft. Inhibition of alanine-serine-cysteine transporter-1 (Asc-1), the primary transporter of d-serine, is attractive because the transporter is localized to neurons in brain regions critical to cognitive function, including the hippocampus and cortical layers III and IV, and is colocalized with d-serine and NMDARs. To identify novel Asc-1 inhibitors, two different screening approaches were performed with whole-cell amino acid uptake in heterologous cells stably expressing human Asc-1: (1) a high-throughput screen (HTS) of 3 M compounds measuring S l-cysteine uptake into cells attached to scintillation proximity assay beads in a 1536 well format and (2) an iterative focused screen (IFS) of a 45 000 compound diversity set using a H d-serine uptake assay with a liquid scintillation plate reader in a 384 well format. Critically important for both screening approaches was the implementation of counter screens to remove nonspecific inhibitors of radioactive amino acid uptake. Furthermore, a 15 000 compound expansion step incorporating both on- and off-target data into chemical and biological fingerprint-based models for selection of additional hits enabled the identification of novel Asc-1-selective chemical matter from the IFS that was not identified in the full-collection HTS.
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http://dx.doi.org/10.1021/acschembio.6b00913DOI Listing
February 2017

Synthesis of Complex Druglike Molecules by the Use of Highly Functionalized Bench-Stable Organozinc Reagents.

Angew Chem Int Ed Engl 2016 10 30;55(44):13714-13718. Epub 2016 Sep 30.

Discovery Chemistry, Process Chemistry, and Structural Chemistry, Merck Research Laboratories, Merck & Co., Inc., West Point, PA, 19486, USA.

The reactivity of a representative set of 17 organozinc pivalates with 18 polyfunctional druglike electrophiles (informers) in Negishi cross-coupling reactions was evaluated by high-throughput experimentation protocols. The high-fidelity scaleup of successful reactions in parallel enabled the isolation of sufficient material for biological testing, thus demonstrating the high value of these new solid zinc reagents in a drug-discovery setting and potentially for many other applications in chemistry. Principal component analysis (PCA) clearly defined the independent roles of the zincates and the informers toward druggable-space coverage.
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http://dx.doi.org/10.1002/anie.201604652DOI Listing
October 2016

Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods.

Chem Sci 2016 Apr 26;7(4):2604-2613. Epub 2016 Jan 26.

Department of Process and Analytical Chemistry , Merck Research Laboratories , Merck and Co., Inc. , Rahway , NJ 07065 , USA . Email:

Major new advances in synthetic chemistry methods are typically reported using simple, non-standardized reaction substrates, and reaction failures are rarely documented. This makes the evaluation and choice of a synthetic method difficult. We report a standardized complex molecule diagnostic approach using collections of relevant drug-like molecules which we call chemistry informer libraries. With this approach, all chemistry results, successes and failures, can be documented to compare and evolve synthetic methods. To aid in the visualization of chemistry results in drug-like physicochemical space we have used an informatics methodology termed principal component analysis. We have validated this method using palladium- and copper-catalyzed reactions, including Suzuki-Miyaura, cyanation and Buchwald-Hartwig amination.
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http://dx.doi.org/10.1039/c5sc04751jDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477042PMC
April 2016

Fragment library design: using cheminformatics and expert chemists to fill gaps in existing fragment libraries.

Methods Mol Biol 2015 ;1289:43-53

Cheminformatics, Merck Research Laboratories, BMB3-146, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA,

Fragment based screening (FBS) has emerged as a mainstream lead discovery strategy in academia, biotechnology start-ups, and large pharma. As a prerequisite of FBS, a structurally diverse library of fragments is desirable in order to identify chemical matter that will interact with the range of diverse target classes that are prosecuted in contemporary screening campaigns. In addition, it is also desirable to offer synthetically amenable starting points to increase the probability of a successful fragment evolution through medicinal chemistry. Herein we describe a method to identify biologically relevant chemical substructures that are missing from an existing fragment library (chemical gaps), and organize these chemical gaps hierarchically so that medicinal chemists can efficiently navigate the prioritized chemical space and subsequently select purchasable fragments for inclusion in an enhanced fragment library.
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http://dx.doi.org/10.1007/978-1-4939-2486-8_5DOI Listing
October 2015

Large scale meta-analysis of fragment-based screening campaigns: privileged fragments and complementary technologies.

J Biomol Screen 2015 Jun 30;20(5):588-96. Epub 2014 Dec 30.

Novartis Institutes for BioMedical Research, Cambridge, MA, USA

A first step in fragment-based drug discovery (FBDD) often entails a fragment-based screen (FBS) to identify fragment "hits." However, the integration of conflicting results from orthogonal screens remains a challenge. Here we present a meta-analysis of 35 fragment-based campaigns at Novartis, which employed a generic 1400-fragment library against diverse target families using various biophysical and biochemical techniques. By statistically interrogating the multidimensional FBS data, we sought to investigate three questions: (1) What makes a fragment amenable for FBS? (2) How do hits from different fragment screening technologies and target classes compare with each other? (3) What is the best way to pair FBS assay technologies? In doing so, we identified substructures that were privileged for specific target classes, as well as fragments that were privileged for authentic activity against many targets. We also revealed some of the discrepancies between technologies. Finally, we uncovered a simple rule of thumb in screening strategy: when choosing two technologies for a campaign, pairing a biochemical and biophysical screen tends to yield the greatest coverage of authentic hits.
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http://dx.doi.org/10.1177/1087057114565080DOI Listing
June 2015

Stitched α-helical peptides via bis ring-closing metathesis.

J Am Chem Soc 2014 Sep 21;136(35):12314-22. Epub 2014 Aug 21.

Department of Chemistry and Chemical Biology and Department of Stem Cell and Regenerative Biology, Harvard University , Cambridge, Massachusetts 02138, United States.

Conformationally stabilized α-helical peptides are capable of inhibiting disease-relevant intracellular or extracellular protein-protein interactions in vivo. We have previously reported that the employment of ring-closing metathesis to introduce a single all-hydrocarbon staple along one face of an α-helical peptide greatly increases α-helical content, binding affinity to a target protein, cell penetration through active transport, and resistance to proteolytic degradation. In an effort to improve upon this technology for stabilizing a peptide in a bioactive α-helical conformation, we report the discovery of an efficient and selective bis ring-closing metathesis reaction leading to peptides bearing multiple contiguous staples connected by a central spiro ring junction. Circular dichroism spectroscopy, NMR, and computational analyses have been used to investigate the conformation of these "stitched" peptides, which are shown to exhibit remarkable thermal stabilities. Likewise, trypsin proteolysis assays confirm the achievement of a structural rigidity unmatched by peptides bearing a single staple. Furthermore, fluorescence-activated cell sorting (FACS) and confocal microscopy assays demonstrate that stitched peptides display superior cell penetrating ability compared to their stapled counterparts, suggesting that this technology may be useful not only in the context of enhancing the drug-like properties of α-helical peptides but also in producing potent agents for the intracellular delivery of proteins and oligonucleotides.
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http://dx.doi.org/10.1021/ja505141jDOI Listing
September 2014

Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes.

J Med Chem 2013 Nov 31;56(21):8879-91. Epub 2013 Oct 31.

Novartis Institutes for Biomedical Research Inc. , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

We introduce a novel strategy to sample bioactive chemical space, which follows-up on hits from fragment campaigns without the need for a crystal structure. Our results strongly suggest that screening a few hundred or thousand fragments can substantially improve the selection of small-molecule screening subsets. By combining fragment-based screening with virtual fragment linking and HTS fingerprints, we have developed an effective strategy not only to expand from low-affinity hits to potent compounds but also to hop in chemical space to substantially novel chemotypes. In benchmark calculations, our approach accessed subsets of compounds that were substantially enriched in chemically diverse hit compounds for various activity classes. Overall, half of the hits in the screening collection were found by screening only 10% of the library. Furthermore, a prospective application led to the discovery of two structurally novel histone deacetylase 4 inhibitors.
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http://dx.doi.org/10.1021/jm401309qDOI Listing
November 2013

Biodiversity of small molecules--a new perspective in screening set selection.

Drug Discov Today 2013 Jul 20;18(13-14):674-80. Epub 2013 Feb 20.

Cheminformatics and Statistics, Hoffmann-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland.

How is the 'diversity' of a compound set defined and how is the most appropriate compound subset identified for assay when screening the entire HTS deck is not an option? A common approach has so far been to cover as much of the chemical space as possible by screening a chemically diverse set of compounds. We show that, rather than chemical diversity, the biologic diversity of a compound library is an essential requirement for hit identification. We describe a simple and efficient approach for the design of a HTS library based on compound-target diversity. Biodiverse compound subsets outperform chemically diverse libraries regarding hit rate and the total number of unique chemical scaffolds present among hits. Specifically, by screening ~19% of a HTS collection, we expect to discover ~50-80% of all desired bioactive compounds.
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http://dx.doi.org/10.1016/j.drudis.2013.02.005DOI Listing
July 2013

Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery.

PLoS One 2012 21;7(11):e48476. Epub 2012 Nov 21.

Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

Medicinal chemists' "intuition" is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists' decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ~4,000 available fragments. Based on each chemist's selections, computational classifiers were built to model each chemist's selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1-2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0048476PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504051PMC
May 2013

Rethinking molecular similarity: comparing compounds on the basis of biological activity.

ACS Chem Biol 2012 Aug 31;7(8):1399-409. Epub 2012 May 31.

Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.

Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.
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http://dx.doi.org/10.1021/cb3001028DOI Listing
August 2012

Enforced presentation of an extrahelical guanine to the lesion recognition pocket of human 8-oxoguanine glycosylase, hOGG1.

J Biol Chem 2012 Jul 16;287(30):24916-28. Epub 2012 Apr 16.

Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA.

A poorly understood aspect of DNA repair proteins is their ability to identify exceedingly rare sites of damage embedded in a large excess of nearly identical undamaged DNA, while catalyzing repair only at the damaged sites. Progress toward understanding this problem has been made by comparing the structures and biochemical behavior of these enzymes when they are presented with either a target lesion or a corresponding undamaged nucleobase. Trapping and analyzing such DNA-protein complexes is particularly difficult in the case of base extrusion DNA repair proteins because of the complexity of the repair reaction, which involves extrusion of the target base from DNA followed by its insertion into the active site where glycosidic bond cleavage is catalyzed. Here we report the structure of a human 8-oxoguanine (oxoG) DNA glycosylase, hOGG1, in which a normal guanine from DNA has been forcibly inserted into the enzyme active site. Although the interactions of the nucleobase with the active site are only subtly different for G versus oxoG, hOGG1 fails to catalyze excision of the normal nucleobase. This study demonstrates that even if hOGG1 mistakenly inserts a normal base into its active site, the enzyme can still reject it on the basis of catalytic incompatibility.
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http://dx.doi.org/10.1074/jbc.M111.316497DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408145PMC
July 2012

Mapping targetable sites on human telomerase RNA pseudoknot/template domain using 2'-OMe RNA-interacting polynucleotide (RIPtide) microarrays.

J Biol Chem 2012 May 26;287(22):18843-53. Epub 2012 Mar 26.

Departmens of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.

Most cellular RNAs engage in intrastrand base-pairing that gives rise to complex three-dimensional folds. This self-pairing presents an impediment toward binding of the RNA by nucleic acid-based ligands. An important step in the discovery of RNA-targeting ligands is therefore to identify those regions in a folded RNA that are accessible toward the nucleic acid-based ligand. Because the folding of RNA targets can involve interactions between nonadjacent regions and employ both Watson-Crick and non-Watson-Crick base-pairing, screening of candidate binder ensembles is typically necessary. Microarray-based screening approaches have shown great promise in this regard and have suggested that achieving complete sequence coverage would be a valuable attribute of a next generation system. Here, we report a custom microarray displaying a library of RNA-interacting polynucleotides comprising all possible 2'-OMe RNA sequences from 4- to 8-nucleotides in length. We demonstrate the utility of this array in identifying RNA-interacting polynucleotides that bind tightly and specifically to the highly conserved, functionally essential template/pseudoknot domain of human telomerase RNA and that inhibit telomerase function in vitro.
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http://dx.doi.org/10.1074/jbc.M111.316596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3365779PMC
May 2012

Chemotography for multi-target SAR analysis in the context of biological pathways.

Bioorg Med Chem 2012 Sep 20;20(18):5416-27. Epub 2012 Feb 20.

Lead Discovery Informatics, Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, MA 02139, USA.

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.
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http://dx.doi.org/10.1016/j.bmc.2012.02.034DOI Listing
September 2012

Structure of the stapled p53 peptide bound to Mdm2.

J Am Chem Soc 2012 Jan 14;134(1):103-6. Epub 2011 Dec 14.

Max Planck Institute for Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.

Mdm2 is a major negative regulator of the tumor suppressor p53 protein, a protein that plays a crucial role in maintaining genome integrity. Inactivation of p53 is the most prevalent defect in human cancers. Inhibitors of the Mdm2-p53 interaction that restore the functional p53 constitute potential nongenotoxic anticancer agents with a novel mode of action. We present here a 2.0 Å resolution structure of the Mdm2 protein with a bound stapled p53 peptide. Such peptides, which are conformationally and proteolytically stabilized with all-hydrocarbon staples, are an emerging class of biologics that are capable of disrupting protein-protein interactions and thus have broad therapeutic potential. The structure represents the first crystal structure of an i, i + 7 stapled peptide bound to its target and reveals that rather than acting solely as a passive conformational brace, a staple can intimately interact with the surface of a protein and augment the binding interface.
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http://dx.doi.org/10.1021/ja2090367DOI Listing
January 2012

De novo design: balancing novelty and confined chemical space.

Expert Opin Drug Discov 2010 Aug 30;5(8):789-812. Epub 2010 Jun 30.

Harvard University, Chemistry and Chemical Biology Department, 12 Oxford Street, Cambridge, MA 02138, USA.

Importance Of The Field: De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

Areas Covered In This Review: This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

What The Reader Will Gain: The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

Take Home Message: De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.
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http://dx.doi.org/10.1517/17460441.2010.497534DOI Listing
August 2010

Introduction of all-hydrocarbon i,i+3 staples into alpha-helices via ring-closing olefin metathesis.

Org Lett 2010 Jul;12(13):3046-9

Department of Chemistry, Harvard University, Cambridge, Massachusetts 02138, USA.

The introduction of all-hydrocarbon i,i+3 staples into alpha-helical peptide scaffolds via ring-closing olefin metathesis (RCM) between two alpha-methyl,alpha-pentenylglycine residues incorporated at i and i+3 positions, which lie on the same face of the helix, has been investigated. The reactions were found to be highly dependent upon the side-chain stereochemistry of the amino acids undergoing RCM. The i,i+3 stapling system established here provides a potentially useful alternative to the well-established i,i+4 stapling system now in widespread use.
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http://dx.doi.org/10.1021/ol1010449DOI Listing
July 2010

FOG: Fragment Optimized Growth algorithm for the de novo generation of molecules occupying druglike chemical space.

J Chem Inf Model 2009 Jul;49(7):1630-42

Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.

An essential feature of all practical de novo molecule generating programs is the ability to focus the potential combinatorial explosion of grown molecules on a desired chemical space. It is a daunting task to balance the generation of new molecules with limitations on growth that produce desired features such as stability in water, synthetic accessibility, or drug-likeness. We have developed an algorithm, Fragment Optimized Growth (FOG), which statistically biases the growth of molecules with desired features. At the heart of the algorithm is a Markov Chain which adds fragments to the nascent molecule in a biased manner, depending on the frequency of specific fragment-fragment connections in the database of chemicals it was trained on. We show that in addition to generating synthetically feasible molecules, it can be trained to grow new molecules that resemble desired classes of molecules such as drugs, natural products, and diversity-oriented synthetic products. In order to classify our grown molecules, we developed the Topology Classifier (TopClass) algorithm that is capable of classifying compounds, for example as drugs or nondrugs. The classification accuracies obtained with TopClass compare favorably with the literature. Furthermore, in contrast to "black-box" approaches such as Neural Networks, TopClass brings to light characteristics of drugs that distinguish them from nondrugs.
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http://dx.doi.org/10.1021/ci9000458DOI Listing
July 2009

All-atom model for stabilization of alpha-helical structure in peptides by hydrocarbon staples.

J Am Chem Soc 2009 Apr;131(13):4622-7

Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.

Recent work has shown that the incorporation of an all-hydrocarbon "staple" into peptides can greatly increase their alpha-helix propensity, leading to an improvement in pharmaceutical properties such as proteolytic stability, receptor affinity, and cell permeability. Stapled peptides thus show promise as a new class of drugs capable of accessing intractable targets such as those that engage in intracellular protein-protein interactions. The extent of alpha-helix stabilization provided by stapling has proven to be substantially context dependent, requiring cumbersome screening to identify the optimal site for staple incorporation. In certain cases, a staple encompassing one turn of the helix (attached at residues i and i+4) furnishes greater helix stabilization than one encompassing two turns (i,i+7 staple), which runs counter to expectation based on polymer theory. These findings highlight the need for a more thorough understanding of the forces that underlie helix stabilization by hydrocarbon staples. Here we report all-atom Monte Carlo folding simulations comparing unmodified peptides derived from RNase A and BID BH3 with various i,i+4 and i,i+7 stapled versions thereof. The results of these simulations were found to be in quantitative agreement with experimentally determined helix propensities. We also discovered that staples can stabilize quasi-stable decoy conformations, and that the removal of these states plays a major role in determining the helix stability of stapled peptides. Finally, we critically investigate why our method works, exposing the underlying physical forces that stabilize stapled peptides.
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http://dx.doi.org/10.1021/ja805037pDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735086PMC
April 2009