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Canham SM, Wang Y, Cornett A, Auld DS, Baeschlin DK, Patoor M, Skaanderup PR, Honda A, Llamas L, Wendel G, Mapa FA, Aspesi P, Labbé-Giguère N, Gamber GG, Palacios DS, Schuffenhauer A, Deng Z, Nigsch F, Frederiksen M, Bushell SM, Rothman D, Jain RK, Hemmerle H, Briner K, Porter JA, Tallarico JA, Jenkins JL. Systematic Chemogenetic Library Assembly. Cell Chem Biol 2020; 27:1124-1129. [PMID: 32707038 DOI: 10.1016/j.chembiol.2020.07.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/03/2020] [Accepted: 07/02/2020] [Indexed: 12/22/2022]
Abstract
Chemogenetic libraries, collections of well-defined chemical probes, provide tremendous value to biomedical research but require substantial effort to ensure diversity as well as quality of the contents. We have assembled a chemogenetic library by data mining and crowdsourcing institutional expertise. We are sharing our approach, lessons learned, and disclosing our current collection of 4,185 compounds with their primary annotated gene targets (https://github.com/Novartis/MoaBox). This physical collection is regularly updated and used broadly both within Novartis and in collaboration with external partners.
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Affiliation(s)
- Stephen M Canham
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Yuan Wang
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Allen Cornett
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Douglas S Auld
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Daniel K Baeschlin
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Maude Patoor
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Philip R Skaanderup
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Ayako Honda
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Luis Llamas
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Greg Wendel
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Felipa A Mapa
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Peter Aspesi
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Nancy Labbé-Giguère
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gabriel G Gamber
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Daniel S Palacios
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Ansgar Schuffenhauer
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Zhan Deng
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Florian Nigsch
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Mathias Frederiksen
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Simon M Bushell
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Deborah Rothman
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rishi K Jain
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Horst Hemmerle
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Karin Briner
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeffery A Porter
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - John A Tallarico
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
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Wang Y, Cornett A, King FJ, Mao Y, Nigsch F, Paris CG, McAllister G, Jenkins JL. Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery. Cell Chem Biol 2016; 23:862-874. [PMID: 27427232 DOI: 10.1016/j.chembiol.2016.05.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 04/29/2016] [Accepted: 05/13/2016] [Indexed: 01/07/2023]
Abstract
The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
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Affiliation(s)
- Yuan Wang
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Allen Cornett
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Fred J King
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA
| | - Yi Mao
- Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
| | - Florian Nigsch
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, Basel 4056, Switzerland
| | - C Gregory Paris
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gregory McAllister
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
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Jaeger S, Min J, Nigsch F, Camargo M, Hutz J, Cornett A, Cleaver S, Buckler A, Jenkins JL. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer. ACTA ACUST UNITED AC 2014; 19:791-802. [PMID: 24518063 DOI: 10.1177/1087057114522690] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 01/14/2014] [Indexed: 02/06/2023]
Abstract
Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
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Affiliation(s)
- Savina Jaeger
- Co-first authors Oncology Translational Medicine, Novartis, Cambridge, MA, USA
| | - Junxia Min
- Co-first authors ONC Target Discovery, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Florian Nigsch
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Basel, Switzerland
| | - Miguel Camargo
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Janna Hutz
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA Pfizer, Cambridge, MA, USA
| | - Allen Cornett
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Stephen Cleaver
- NIBR IT, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Alan Buckler
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
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Petrone PM, Simms B, Nigsch F, Lounkine E, Kutchukian P, Cornett A, Deng Z, Davies JW, Jenkins JL, Glick M. Rethinking molecular similarity: comparing compounds on the basis of biological activity. ACS Chem Biol 2012; 7:1399-409. [PMID: 22594495 DOI: 10.1021/cb3001028] [Citation(s) in RCA: 113] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
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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Affiliation(s)
- Paula M. Petrone
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Benjamin Simms
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Florian Nigsch
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Eugen Lounkine
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Kutchukian
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Allen Cornett
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Zhan Deng
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - John W. Davies
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Meir Glick
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
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Sukuru SCK, Nigsch F, Quancard J, Renatus M, Chopra R, Brooijmans N, Mikhailov D, Deng Z, Cornett A, Jenkins JL, Hommel U, Davies JW, Glick M. A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space. Protein Sci 2011; 19:2096-109. [PMID: 20799349 DOI: 10.1002/pro.490] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.
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Affiliation(s)
- Sai Chetan K Sukuru
- Lead Discovery Informatics, Lead Finding Platform, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
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Maguire LC, Mendiondo OA, Medina WD, Cronin JD, Meeker WR, Cornett A. Infusion chemotherapy and radiation therapy: a new approach with encouraging results in anal, esophageal, head and neck and cervical carcinoma. J Ky Med Assoc 1985; 83:653-5. [PMID: 4078485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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