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Towards Arginase Inhibition: Hybrid SAR Protocol for Property Mapping of Chlorinated N-arylcinnamamides. Int J Mol Sci 2023; 24:ijms24043611. [PMID: 36835023 PMCID: PMC9968098 DOI: 10.3390/ijms24043611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
A series of seventeen 4-chlorocinnamanilides and seventeen 3,4-dichlorocinnamanilides were characterized for their antiplasmodial activity. In vitro screening on a chloroquine-sensitive strain of Plasmodium falciparum 3D7/MRA-102 highlighted that 23 compounds possessed IC50 < 30 µM. Typically, 3,4-dichlorocinnamanilides showed a broader range of activity compared to 4-chlorocinnamanilides. (2E)-N-[3,5-bis(trifluoromethyl)phenyl]-3-(3,4-dichlorophenyl)prop-2-en-amide with IC50 = 1.6 µM was the most effective agent, while the other eight most active derivatives showed IC50 in the range from 1.8 to 4.6 µM. A good correlation between the experimental logk and the estimated clogP was recorded for the whole ensemble of the lipophilicity generators. Moreover, the SAR-mediated similarity assessment of the novel (di)chlorinated N-arylcinnamamides was conducted using the collaborative (hybrid) ligand-based and structure-related protocols. In consequence, an 'averaged' selection-driven interaction pattern was produced based in namely 'pseudo-consensus' 3D pharmacophore mapping. The molecular docking approach was engaged for the most potent antiplasmodial agents in order to gain an insight into the arginase-inhibitor binding mode. The docking study revealed that (di)chlorinated aromatic (C-phenyl) rings are oriented towards the binuclear manganese cluster in the energetically favorable poses of the chloroquine and the most potent arginase inhibitors. Additionally, the water-mediated hydrogen bonds were formed via carbonyl function present in the new N-arylcinnamamides and the fluorine substituent (alone or in trifluoromethyl group) of N-phenyl ring seems to play a key role in forming the halogen bonds.
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2
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Pyasi S, Jonniya NA, Sk MF, Nayak D, Kar P. Finding potential inhibitors against RNA-dependent RNA polymerase (RdRp) of bovine ephemeral fever virus (BEFV): an in- silico study. J Biomol Struct Dyn 2022; 40:10403-10421. [PMID: 34238122 DOI: 10.1080/07391102.2021.1946714] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The bovine ephemeral fever virus (BEFV) is an enzootic agent that affects millions of bovines and causes major economic losses. Though the virus is seasonally reported with a very high morbidity rate (80-100%) from African, Australian, and Asiatic continents, it remains a neglected pathogen in many of its endemic areas, with no proper therapeutic drugs or vaccines presently available for treatment. The RNA-dependent RNA polymerase (RdRp) catalyzes the viral RNA synthesis and is an appropriate candidate for antiviral drug developments. We utilized integrated computational tools to build the 3D model of BEFV-RdRp and then predicted its probable active binding sites. The virtual screening and optimization against these active sites, using several small-molecule inhibitors from a different category of Life Chemical database and FDA-approved drugs from the ZINC database, was performed. We found nine molecules that have docking scores varying between -6.84 to -10.43 kcal/mol. Furthermore, these complexes were analyzed for their conformational dynamics and thermodynamic stability using molecular dynamics simulations in conjunction with the molecular mechanics generalized Born surface area (MM-GBSA) scheme. The binding free energy calculations depict that the electrostatic interactions play a dominant role in the RdRp-inhibitor binding. The hot spot residues, such as Arg565, Asp631, Glu633, Asp740, and Glu707, were found to control the RdRp-inhibitor interaction. The ADMET analysis strongly suggests favorable pharmacokinetics of these compounds that may prove useful for treating the BEFV ailment. Overall, we anticipate that these findings would help explore and develop a wide range of anti-BEFV therapy.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shruti Pyasi
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Nisha Amarnath Jonniya
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Md Fulbabu Sk
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Debasis Nayak
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Parimal Kar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
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3
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Hricovíni M, Owens RJ, Bak A, Kozik V, Musiał W, Pierattelli R, Májeková M, Rodríguez Y, Musioł R, Slodek A, Štarha P, Piętak K, Słota D, Florkiewicz W, Sobczak-Kupiec A, Jampílek J. Chemistry towards Biology-Instruct: Snapshot. Int J Mol Sci 2022; 23:14815. [PMID: 36499140 PMCID: PMC9739621 DOI: 10.3390/ijms232314815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/15/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
The knowledge of interactions between different molecules is undoubtedly the driving force of all contemporary biomedical and biological sciences. Chemical biology/biological chemistry has become an important multidisciplinary bridge connecting the perspectives of chemistry and biology to the study of small molecules/peptidomimetics and their interactions in biological systems. Advances in structural biology research, in particular linking atomic structure to molecular properties and cellular context, are essential for the sophisticated design of new medicines that exhibit a high degree of druggability and very importantly, druglikeness. The authors of this contribution are outstanding scientists in the field who provided a brief overview of their work, which is arranged from in silico investigation through the characterization of interactions of compounds with biomolecules to bioactive materials.
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Affiliation(s)
- Miloš Hricovíni
- Institute of Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, 845 38 Bratislava, Slovakia
| | - Raymond J. Owens
- Structural Biology, The Rosalind Franklin Institute, Harwell Science Campus, UK, University of Oxford, Oxford OX11 0QS, UK
- Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Andrzej Bak
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland
| | - Violetta Kozik
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland
| | - Witold Musiał
- Department of Physical Chemistry and Biophysics, Pharmaceutical Faculty, Wroclaw Medical University, Borowska 211A, 50 556 Wrocław, Poland
| | - Roberta Pierattelli
- Magnetic Resonance Center and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Magdaléna Májeková
- Center of Experimental Medicine SAS and Department of Biochemical Pharmacology, Institute of Experimental Pharmacology and Toxicology, Slovak Academy of Sciences, Dubravska cesta 9, 841 04 Bratislava, Slovakia
| | - Yoel Rodríguez
- Department of Natural Sciences, Eugenio María de Hostos Community College, City University of New York, 500 Grand Concourse, Bronx, NY 10451, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
| | - Robert Musioł
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland
| | - Aneta Slodek
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland
| | - Pavel Štarha
- Department of Inorganic Chemistry, Faculty of Science, Palacký University Olomouc, 17. listopadu 1192/12, 771 46 Olomouc, Czech Republic
| | - Karina Piętak
- Department of Materials Science, Faculty of Materials Engineering and Physics, Cracow University of Technology, 37 Jana Pawła II Av., 31 864 Krakow, Poland
| | - Dagmara Słota
- Department of Materials Science, Faculty of Materials Engineering and Physics, Cracow University of Technology, 37 Jana Pawła II Av., 31 864 Krakow, Poland
| | - Wioletta Florkiewicz
- Department of Materials Science, Faculty of Materials Engineering and Physics, Cracow University of Technology, 37 Jana Pawła II Av., 31 864 Krakow, Poland
| | - Agnieszka Sobczak-Kupiec
- Department of Materials Science, Faculty of Materials Engineering and Physics, Cracow University of Technology, 37 Jana Pawła II Av., 31 864 Krakow, Poland
| | - Josef Jampílek
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovičova 6, 842 15 Bratislava, Slovakia
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4
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Blay V, Radivojevic T, Allen JE, Hudson CM, Garcia Martin H. MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design. J Chem Inf Model 2022; 62:3551-3564. [PMID: 35857932 PMCID: PMC9364320 DOI: 10.1021/acs.jcim.2c00229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
![]()
The growing capabilities of synthetic biology and organic
chemistry
demand tools to guide syntheses toward useful molecules. Here, we
present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that
uses a novel approach to generate molecules predicted to meet a desired
property specification (e.g., a binding affinity of 50 nM or an octane
number of 90). MACAW describes molecules by embedding them into a
smooth multidimensional numerical space, avoiding uninformative dimensions
that previous methods often introduce. The coordinates in this embedding
provide a natural choice of features for accurately predicting molecular
properties, which we demonstrate with examples for cetane and octane
numbers, flash points, and histamine H1 receptor binding affinity.
The approach is computationally efficient and well-suited to the small-
and medium-size datasets commonly used in biosciences. We showcase
the utility of MACAW for virtual screening by identifying molecules
with high predicted binding affinity to the histamine H1 receptor
and limited affinity to the muscarinic M2 receptor, which are targets
of medicinal relevance. Combining these predictive capabilities with
a novel generative algorithm for molecules allows us to recommend
molecules with a desired property value (i.e., inverse molecular design).
We demonstrate this capability by recommending molecules with predicted
octane numbers of 40, 80, and 120, which is an important characteristic
of biofuels. Thus, MACAW augments classical retrosynthesis tools by
providing recommendations for molecules on specification.
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Affiliation(s)
- Vincent Blay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Tijana Radivojevic
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States.,DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Jonathan E Allen
- Global Security Computing Applications, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Corey M Hudson
- Sandia National Laboratories, Livermore, California 94550, United States
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States.,DOE Agile BioFoundry, Emeryville, California 94608, United States
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5
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Kell DB. The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes. Molecules 2021; 26:5629. [PMID: 34577099 PMCID: PMC8470029 DOI: 10.3390/molecules26185629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/03/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK;
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
- Mellizyme Biotechnology Ltd., IC1, Liverpool Science Park, Mount Pleasant, Liverpool L3 5TF, UK
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6
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Szilágyi K, Flachner B, Hajdú I, Szaszkó M, Dobi K, Lőrincz Z, Cseh S, Dormán G. Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening. Molecules 2021; 26:5593. [PMID: 34577064 PMCID: PMC8468386 DOI: 10.3390/molecules26185593] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 12/23/2022] Open
Abstract
Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds' databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.
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Affiliation(s)
| | | | | | | | | | | | | | - György Dormán
- TargetEx Ltd., Madách I. u. 31/2, 2120 Dunakeszi, Hungary; (K.S.); (B.F.); (I.H.); (M.S.); (K.D.); (Z.L.); (S.C.)
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7
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Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? Int J Mol Sci 2021; 22:ijms22105212. [PMID: 34069090 PMCID: PMC8156896 DOI: 10.3390/ijms22105212] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023] Open
Abstract
A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.
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8
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Design, Synthesis, and Evaluation of Novel 3-Carboranyl-1,8-Naphthalimide Derivatives as Potential Anticancer Agents. Int J Mol Sci 2021; 22:ijms22052772. [PMID: 33803403 PMCID: PMC7967199 DOI: 10.3390/ijms22052772] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 12/11/2022] Open
Abstract
We synthesized a series of novel 3-carboranyl-1,8-naphthalimide derivatives, mitonafide and pinafide analogs, using click chemistry, reductive amination and amidation reactions and investigated their in vitro effects on cytotoxicity, cell death, cell cycle, and the production of reactive oxygen species in a HepG2 cancer cell line. The analyses showed that modified naphthalic anhydrides and naphthalimides bearing ortho- or meta-carboranes exhibited diversified activity. Naphthalimides were more cytotoxic than naphthalic anhydrides, with the highest IC50 value determined for compound 9 (3.10 µM). These compounds were capable of inducing cell cycle arrest at G0/G1 or G2M phase and promoting apoptosis, autophagy or ferroptosis. The most promising conjugate 35 caused strong apoptosis and induced ROS production, which was proven by the increased level of 2′-deoxy-8-oxoguanosine in DNA. The tested conjugates were found to be weak topoisomerase II inhibitors and classical DNA intercalators. Compounds 33, 34, and 36 fluorescently stained lysosomes in HepG2 cells. Additionally, we performed a similarity-based assessment of the property profile of the conjugates using the principal component analysis. The creation of an inhibitory profile and descriptor-based plane allowed forming a structure–activity landscape. Finally, a ligand-based comparative molecular field analysis was carried out to specify the (un)favorable structural modifications (pharmacophoric pattern) that are potentially important for the quantitative structure–activity relationship modeling of the carborane–naphthalimide conjugates.
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9
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Kos J, Bak A, Kozik V, Jankech T, Strharsky T, Swietlicka A, Michnova H, Hosek J, Smolinski A, Oravec M, Devinsky F, Hutta M, Jampilek J. Biological Activities and ADMET-Related Properties of Novel Set of Cinnamanilides. Molecules 2020; 25:molecules25184121. [PMID: 32916979 PMCID: PMC7570544 DOI: 10.3390/molecules25184121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022] Open
Abstract
A series of nineteen novel ring-substituted N-arylcinnamanilides was synthesized and characterized. All investigated compounds were tested against Staphylococcus aureus as the reference strain, two clinical isolates of methicillin-resistant S. aureus (MRSA), and Mycobacterium tuberculosis. (2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide showed even better activity (minimum inhibitory concentration (MIC) 25.9 and 12.9 µM) against MRSA isolates than the commonly used ampicillin (MIC 45.8 µM). The screening of the cell viability was performed using THP1-Blue™ NF-κB cells and, except for (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (IC50 6.5 µM), none of the discussed compounds showed any significant cytotoxic effect up to 20 μM. Moreover, all compounds were tested for their anti-inflammatory potential; several compounds attenuated the lipopolysaccharide-induced NF-κB activation and were more potent than the parental cinnamic acid. The lipophilicity values were specified experimentally as well. In addition, in silico approximation of the lipophilicity values was performed employing a set of free/commercial clogP estimators, corrected afterwards by the corresponding pKa calculated at physiological pH and subsequently cross-compared with the experimental parameters. The similarity-driven property space evaluation of structural analogs was carried out using the principal component analysis, Tanimoto metrics, and Kohonen mapping.
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Affiliation(s)
- Jiri Kos
- Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic; (J.K.); (T.S.); (H.M.); (J.H.)
| | - Andrzej Bak
- Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland; (V.K.); (A.S.)
- Correspondence: (A.B.); (J.J.)
| | - Violetta Kozik
- Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland; (V.K.); (A.S.)
| | - Timotej Jankech
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, 84215 Bratislava, Slovakia; (T.J.); (M.H.)
| | - Tomas Strharsky
- Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic; (J.K.); (T.S.); (H.M.); (J.H.)
| | - Aleksandra Swietlicka
- Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland; (V.K.); (A.S.)
| | - Hana Michnova
- Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic; (J.K.); (T.S.); (H.M.); (J.H.)
| | - Jan Hosek
- Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic; (J.K.); (T.S.); (H.M.); (J.H.)
| | - Adam Smolinski
- Central Mining Institute, Pl. Gwarkow 1, 40166 Katowice, Poland;
| | - Michal Oravec
- Global Change Research Institute CAS, Belidla 986/4a, 60300 Brno, Czech Republic;
| | - Ferdinand Devinsky
- Faculty of Pharmacy, Comenius University, Odbojarov 10, 83232 Bratislava, Slovakia;
| | - Milan Hutta
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, 84215 Bratislava, Slovakia; (T.J.); (M.H.)
| | - Josef Jampilek
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, 84215 Bratislava, Slovakia; (T.J.); (M.H.)
- Correspondence: (A.B.); (J.J.)
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10
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Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides. Int J Mol Sci 2020; 21:ijms21186583. [PMID: 32916824 PMCID: PMC7555178 DOI: 10.3390/ijms21186583] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 02/07/2023] Open
Abstract
A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity was observed for the combinations of ciprofloxacin with N-[4-bromo-3-(trifluoromethyl)phenyl]- and N-(4-bromo-3-fluorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate. The similarity-related property space assessment for the congeneric series of structurally related carboxamide derivatives was performed using the principal component analysis. Interestingly, different distribution of mono-halogenated carboxamide derivatives with the –CF3 substituent is accompanied by the increased activity profile. A symmetric matrix of Tanimoto coefficients indicated the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones. Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed structural modifications that are valid for determining activity cliffs. Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the functional group.
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11
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Aguirre NF, Morgenstern A, Cawkwell MJ, Batista ER, Yang P. Development of Density Functional Tight-Binding Parameters Using Relative Energy Fitting and Particle Swarm Optimization. J Chem Theory Comput 2020; 16:1469-1481. [DOI: 10.1021/acs.jctc.9b00880] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Néstor F. Aguirre
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Amanda Morgenstern
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - M. J. Cawkwell
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Enrique R. Batista
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ping Yang
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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12
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Abstract
The ccbmlib Python package is a collection of modules for modeling similarity value distributions based on Tanimoto coefficients for fingerprints available in RDKit. It can be used to assess the statistical significance of Tanimoto coefficients and evaluate how molecular similarity is reflected when different fingerprint representations are used. Significance measures derived from p-values allow a quantitative comparison of similarity scores obtained from different fingerprint representations that might have very different value ranges. Furthermore, the package models conditional distributions of similarity coefficients for a given reference compound. The conditional significance score estimates where a test compound would be ranked in a similarity search. The models are based on the statistical analysis of feature distributions and feature correlations of fingerprints of a reference database. The resulting models have been evaluated for 11 RDKit fingerprints, taking a collection of ChEMBL compounds as a reference data set. For most fingerprints, highly accurate models were obtained, with differences of 1% or less for Tanimoto coefficients indicating high similarity.
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Affiliation(s)
- Martin Vogt
- Department of Life Science Informatics, B-IT, University of Bonn, Endenicher Allee 19c, Bonn, NRW, 53115, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, University of Bonn, Endenicher Allee 19c, Bonn, NRW, 53115, Germany
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13
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Vogt M, Bajorath J. ccbmlib - a Python package for modeling Tanimoto similarity value distributions. F1000Res 2020; 9:Chem Inf Sci-100. [PMID: 32161645 PMCID: PMC7050271 DOI: 10.12688/f1000research.22292.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/04/2020] [Indexed: 11/15/2023] Open
Abstract
The ccbmlib Python package is a collection of modules for modeling similarity value distributions based on Tanimoto coefficients for fingerprints available in RDKit. It can be used to assess the statistical significance of Tanimoto coefficients and evaluate how molecular similarity is reflected when different fingerprint representations are used. Significance measures derived from p-values allow a quantitative comparison of similarity scores obtained from different fingerprint representations that might have very different value ranges. Furthermore, the package models conditional distributions of similarity coefficients for a given reference compound. The conditional significance score estimates where a test compound would be ranked in a similarity search. The models are based on the statistical analysis of feature distributions and feature correlations of fingerprints of a reference database. The resulting models have been evaluated for 11 RDKit fingerprints, taking a collection of ChEMBL compounds as a reference data set. For most fingerprints, highly accurate models were obtained, with differences of 1% or less for Tanimoto coefficients indicating high similarity.
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Affiliation(s)
- Martin Vogt
- Department of Life Science Informatics, B-IT, University of Bonn, Endenicher Allee 19c, Bonn, NRW, 53115, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, University of Bonn, Endenicher Allee 19c, Bonn, NRW, 53115, Germany
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14
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Martinez-Mayorga K, Madariaga-Mazon A, Medina-Franco JL, Maggiora G. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opin Drug Discov 2020; 15:293-306. [PMID: 31965870 DOI: 10.1080/17460441.2020.1696307] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.
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Affiliation(s)
| | | | - José L Medina-Franco
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
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15
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Bak A, Pizova H, Kozik V, Vorcakova K, Kos J, Treml J, Odehnalova K, Oravec M, Imramovsky A, Bobal P, Smolinski A, Trávníček Z, Jampilek J. SAR-mediated Similarity Assessment of the Property Profile for New, Silicon-Based AChE/BChE Inhibitors. Int J Mol Sci 2019; 20:E5385. [PMID: 31671776 PMCID: PMC6862691 DOI: 10.3390/ijms20215385] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 10/25/2019] [Accepted: 10/27/2019] [Indexed: 12/20/2022] Open
Abstract
A set of 25 novel, silicon-based carbamate derivatives as potential acetyl- and butyrylcholinesterase (AChE/BChE) inhibitors was synthesized and characterized by their in vitro inhibition profiles and the selectivity indexes (SIs). The prepared compounds were also tested for their inhibition potential on photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. In fact, some of the newly prepared molecules revealed comparable or even better inhibitory activities compared to the marketed drugs (rivastigmine or galanthamine) and commercially applied pesticide Diuron®, respectively. Generally, most compounds exhibited better inhibition potency towards AChE; however, a wider activity span was observed for BChE. Notably, benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-hydroxyphenyl)carbamoyl]ethyl]-carbamate (2) and benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-hydroxyphenyl)carbamoyl]ethyl]-carbamate (3) were characterized by fairly high selective indexes. Specifically, compound 2 was prescribed with the lowest IC50 value that corresponds quite well with galanthamine inhibition activity, while the inhibitory profiles of molecules 3 and benzyl-N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-hydroxyphenyl)carbamoyl]ethyl]carbamate (4) are in line with rivastigmine activity. Moreover, a structure-activity relationship (SAR)-driven similarity evaluation of the physicochemical properties for the carbamates examined appeared to have foreseen the activity cliffs using a similarity-activity landscape index for BChE inhibitory response values. The 'indirect' ligand-based and 'direct' protein-mediated in silico approaches were applied to specify electronic/steric/lipophilic factors that are potentially valid for quantitative (Q)SAR modeling of the carbamate analogues. The stochastic model validation was used to generate an 'average' 3D-QSAR pharmacophore pattern. Finally, the target-oriented molecular docking was employed to (re)arrange the spatial distribution of the ligand property space for BChE and photosystem II (PSII).
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Affiliation(s)
- Andrzej Bak
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland.
| | - Hana Pizova
- Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic.
| | - Violetta Kozik
- Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland.
| | - Katarina Vorcakova
- Department of Biological and Biochemical Sciences, Faculty of Chemical Technology, University of Pardubice, Studentska 573, 532 10 Pardubice, Czech Republic.
| | - Jiri Kos
- Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic, (J.K.).
| | - Jakub Treml
- Department of Molecular Biology and Pharmaceutical Biotechnology, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic.
| | - Klara Odehnalova
- Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic.
| | - Michal Oravec
- Global Change Research Institute CAS, Belidla 986/4a, 60300 Brno, Czech Republic.
| | - Ales Imramovsky
- Institute of Organic Chemistry and Technology, Faculty of Chemical Technology, University of Pardubice, Studentska 573, 532 10 Pardubice, Czech Republic.
| | - Pavel Bobal
- Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic.
| | - Adam Smolinski
- Department of Energy Saving and Air Protection, Central Mining Institute, Plac Gwarkow 1, 40 166 Katowice, Poland.
| | - Zdeněk Trávníček
- Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic, (J.K.).
| | - Josef Jampilek
- Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic, (J.K.).
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16
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Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches. Molecules 2019; 24:molecules24203641. [PMID: 31600984 PMCID: PMC6832932 DOI: 10.3390/molecules24203641] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/03/2019] [Accepted: 10/05/2019] [Indexed: 01/24/2023] Open
Abstract
The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.
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17
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Ebejer JP, Finn PW, Wong WK, Deane CM, Morris GM. Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening. J Chem Inf Model 2019; 59:2600-2616. [PMID: 31117509 PMCID: PMC7007185 DOI: 10.1021/acs.jcim.8b00779] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.
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Affiliation(s)
- Jean-Paul Ebejer
- Centre for Molecular Medicine and Biobanking , University of Malta , Msida , MSD 2080 , Malta
| | - Paul W Finn
- Oxford Drug Design Limited, Oxford Centre for Innovation , New Road , Oxford OX1 1BY , U.K.,The School of Computing , University of Buckingham , Hunter Street , Buckingham , MK18 1EG , U.K
| | - Wing Ki Wong
- Oxford Protein Informatics Group, Department of Statistics , University of Oxford , 24-29 St. Giles' , Oxford , OX1 3LB , U.K
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics , University of Oxford , 24-29 St. Giles' , Oxford , OX1 3LB , U.K
| | - Garrett M Morris
- Oxford Protein Informatics Group, Department of Statistics , University of Oxford , 24-29 St. Giles' , Oxford , OX1 3LB , U.K
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18
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Awale M, Reymond JL. Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning. J Chem Inf Model 2018; 59:10-17. [PMID: 30558418 DOI: 10.1021/acs.jcim.8b00524] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4) and features an unprecedented combination of nearest neighbor (NN) searches and Naı̈ve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at http://gdb.unibe.ch .
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure , University of Berne , Freiestrasse 3 , 3012 Berne , Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure , University of Berne , Freiestrasse 3 , 3012 Berne , Switzerland
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19
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Shultz MD. Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs. J Med Chem 2018; 62:1701-1714. [DOI: 10.1021/acs.jmedchem.8b00686] [Citation(s) in RCA: 177] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Michael D. Shultz
- Global Discovery Chemistry, Novartis Institutes for Biomedical Research, Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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20
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Mazalan L, Bell A, Sbaffi L, Willett P. Cross-Classified Multilevel Modelling of the Effectiveness of Similarity-Based Virtual Screening. ChemMedChem 2018; 13:582-587. [PMID: 29106074 DOI: 10.1002/cmdc.201700487] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Indexed: 11/06/2022]
Abstract
The screening effectiveness of a chemical similarity search depends on a range of factors, including the bioactivity of interest, the types of similarity coefficient and fingerprint that comprise the similarity measure, and the nature of the reference structure that is being searched against a database. This study introduces the use of cross-classified multilevel modelling as a way to investigate the relative importance of these four factors when carrying out similarity searches on the ChEMBL database. Two principal conclusions can be drawn from the analyses: that the fingerprint plays a more important role than the similarity coefficient in determining the effectiveness of a similarity search, and that comparative studies of similarity measures should involve many more reference structures than has been the case in much previous work.
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Affiliation(s)
- Lucyantie Mazalan
- Information School, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK
| | - Andrew Bell
- Sheffield Methods Institute, University of Sheffield, 219 Portobello, Sheffield, S1 4DP, UK
| | - Laura Sbaffi
- Information School, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK
| | - Peter Willett
- Information School, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK
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21
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Carbonell P, Koch M, Duigou T, Faulon JL. Enzyme Discovery: Enzyme Selection and Pathway Design. Methods Enzymol 2018; 608:3-27. [PMID: 30173766 DOI: 10.1016/bs.mie.2018.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom; Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France; School of Chemistry, The University of Manchester, Manchester, United Kingdom.
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22
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Awale M, Probst D, Reymond JL. WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces. J Chem Inf Model 2017; 57:643-649. [PMID: 28316236 DOI: 10.1021/acs.jcim.6b00690] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The concept of chemical space provides a convenient framework to analyze large collections of molecules by placing them in property spaces where distances represent similarities. Here we report webMolCS, a new type of web-based interface visualizing up to 5000 user-defined molecules in six different three-dimensional (3D) chemical spaces obtained by principal component analysis or similarity mapping of multidimensional property spaces describing composition (MQN: 42D molecular quantum numbers, SMIfp: 34D SMILES fingerprint), shapes and pharmacophores (APfp: 20D atom pair fingerprint, Xfp: 55D category extended atom pair fingerprint), and substructures (Sfp: 1024D binary substructure fingerprint, ECfp4:1024D extended connectivity fingerprint). Each molecule is shown as a sphere, and its structure appears on mouse over. The sphere is color-coded by similarity to the first compound in the list, by the list rank, or by a user-defined value, which reveals the relationship between any property encoded by these values and structural similarities. WebMolCS is freely available at www.gdb.unibe.ch .
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Daniel Probst
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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23
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O'Hagan S, Kell DB. Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures. J Cheminform 2017; 9:18. [PMID: 28316656 PMCID: PMC5344883 DOI: 10.1186/s13321-017-0198-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 02/09/2017] [Indexed: 12/21/2022] Open
Abstract
In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Douglas B Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
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24
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González-Medina M, Méndez-Lucio O, Medina-Franco JL. Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure-Activity Relationships. J Chem Inf Model 2017; 57:397-402. [PMID: 28234475 DOI: 10.1021/acs.jcim.6b00776] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Activity landscape modeling is a powerful method for the quantitative analysis of structure-activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access. Herein, we present Activity Landscape Plotter as the first freely available web-based tool to automatically analyze structure-activity relationships of compound data sets. Based on the concept of activity landscape modeling, the online service performs pairwise structure and activity relationships from an input data set supplied by the user. For visual analysis, Activity Landscape Plotter generates Structure-Activity Similarity and Dual-Activity Difference maps. The user can interactively navigate through the maps and export all the pairwise structure-activity information as comma delimited files. Activity Landscape Plotter is freely accessible at https://unam-shiny-difacquim.shinyapps.io/ActLSmaps /.
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Affiliation(s)
- Mariana González-Medina
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Oscar Méndez-Lucio
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México , Avenida Universidad 3000, Mexico City 04510, Mexico
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25
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O'Hagan S, Kell DB. MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites. Front Pharmacol 2016; 7:266. [PMID: 27597830 PMCID: PMC4992690 DOI: 10.3389/fphar.2016.00266] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 08/08/2016] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Previous studies compared the molecular similarity of marketed drugs and endogenous human metabolites (endogenites), using a series of fingerprint-type encodings, variously ranked and clustered using the Tanimoto (Jaccard) similarity coefficient (TS). Because this gives equal weight to all parts of the encoding (thence to different substructures in the molecule) it may not be optimal, since in many cases not all parts of the molecule will bind to their macromolecular targets. Unsupervised methods cannot alone uncover this. We here explore the kinds of differences that may be observed when the TS is replaced-in a manner more equivalent to semi-supervised learning-by variants of the asymmetric Tversky (TV) similarity, that includes α and β parameters. RESULTS Dramatic differences are observed in (i) the drug-endogenite similarity heatmaps, (ii) the cumulative "greatest similarity" curves, and (iii) the fraction of drugs with a Tversky similarity to a metabolite exceeding a given value when the Tversky α and β parameters are varied from their Tanimoto values. The same is true when the sum of the α and β parameters is varied. A clear trend toward increased endogenite-likeness of marketed drugs is observed when α or β adopt values nearer the extremes of their range, and when their sum is smaller. The kinds of molecules exhibiting the greatest similarity to two interrogating drug molecules (chlorpromazine and clozapine) also vary in both nature and the values of their similarity as α and β are varied. The same is true for the converse, when drugs are interrogated with an endogenite. The fraction of drugs with a Tversky similarity to a molecule in a library exceeding a given value depends on the contents of that library, and α and β may be "tuned" accordingly, in a semi-supervised manner. At some values of α and β drug discovery library candidates or natural products can "look" much more like (i.e., have a numerical similarity much closer to) drugs than do even endogenites. CONCLUSIONS Overall, the Tversky similarity metrics provide a more useful range of examples of molecular similarity than does the simpler Tanimoto similarity, and help to draw attention to molecular similarities that would not be recognized if Tanimoto alone were used. Hence, the Tversky similarity metrics are likely to be of significant value in many general problems in cheminformatics.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
| | - Douglas B. Kell
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
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Awale M, Reymond JL. Web-based 3D-visualization of the DrugBank chemical space. J Cheminform 2016; 8:25. [PMID: 27148409 PMCID: PMC4855437 DOI: 10.1186/s13321-016-0138-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 04/27/2016] [Indexed: 12/14/2022] Open
Abstract
Background Similarly to the periodic table for elements, chemical space offers an organizing principle for representing the diversity of organic molecules, usually in the form of multi-dimensional property spaces that are subjected to dimensionality reduction methods to obtain 3D-spaces or 2D-maps suitable for visual inspection. Unfortunately, tools to look at chemical space on the internet are currently very limited. Results Herein we present webDrugCS, a web application freely available at www.gdb.unibe.ch to visualize DrugBank (www.drugbank.ca, containing over 6000 investigational and approved drugs) in five different property spaces. WebDrugCS displays 3D-clouds of color-coded grid points representing molecules, whose structural formula is displayed on mouse over with an option to link to the corresponding molecule page at the DrugBank website. The 3D-clouds are obtained by principal component analysis of high dimensional property spaces describing constitution and topology (42D molecular quantum numbers MQN), structural features (34D SMILES fingerprint SMIfp), molecular shape (20D atom pair fingerprint APfp), pharmacophores (55D atom category extended atom pair fingerprint Xfp) and substructures (1024D binary substructure fingerprint Sfp). User defined molecules can be uploaded as SMILES lists and displayed together with DrugBank. In contrast to 2D-maps where many compounds fold onto each other, these 3D-spaces have a comparable resolution to their parent high-dimensional chemical space. Conclusion To the best of our knowledge webDrugCS is the first publicly available web tool for interactive visualization and exploration of the DrugBank chemical space in 3D. WebDrugCS works on computers, tablets and phones, and facilitates the visual exploration of DrugBank to rapidly learn about the structural diversity of small molecule drugs.webDrugCS visualization of DrugBank projected in 3D MQN space color-coded by ring count, with pointer showing the drug 5-fluorouracil. ![]()
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
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ITPI: Initial Transcription Process-Based Identification Method of Bioactive Components in Traditional Chinese Medicine Formula. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2016; 2016:8250323. [PMID: 27034696 PMCID: PMC4789420 DOI: 10.1155/2016/8250323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 02/09/2016] [Accepted: 02/11/2016] [Indexed: 12/22/2022]
Abstract
Identification of bioactive components is an important area of research in traditional Chinese medicine (TCM) formula. The reported identification methods only consider the interaction between the components and the target proteins, which is not sufficient to explain the influence of TCM on the gene expression. Here, we propose the Initial Transcription Process-based Identification (ITPI) method for the discovery of bioactive components that influence transcription factors (TFs). In this method, genome-wide chip detection technology was used to identify differentially expressed genes (DEGs). The TFs of DEGs were derived from GeneCards. The components influencing the TFs were derived from STITCH. The bioactive components in the formula were identified by evaluating the molecular similarity between the components in formula and the components that influence the TF of DEGs. Using the formula of Tian-Zhu-San (TZS) as an example, the reliability and limitation of ITPI were examined and 16 bioactive components that influence TFs were identified.
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Rivera-Borroto OM, García-de la Vega JM, Marrero-Ponce Y, Grau R. Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:158-67. [PMID: 26886740 DOI: 10.1109/tcbb.2015.2424435] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto's coefficient has been claimed to be the most prominent measure for this task. Nevertheless, this field is far from being exhausted since the computer science no free lunch theorem predicts that "no similarity measure has overall superiority over the population of data sets". We introduce 12 relational agreement (RA) coefficients for seven metric scales, which are integrated within a group fusion-based similarity searching algorithm. These similarity measures are compared to a reference panel of 21 proximity quantifiers over 17 benchmark data sets (MUV), by using informative descriptors, a feature selection stage, a suitable performance metric, and powerful comparison tests. In this stage, RA coefficients perform favourably with repect to the state-of-the-art proximity measures. Afterward, the RA-based method outperform another four nearest neighbor searching algorithms over the same data domains. In a third validation stage, RA measures are successfully applied to the virtual screening of the NCI data set. Finally, we discuss a possible molecular interpretation for these similarity variants.
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Erazo T, Lorente M, López-Plana A, Muñoz-Guardiola P, Fernández-Nogueira P, García-Martínez JA, Bragado P, Fuster G, Salazar M, Espadaler J, Hernández-Losa J, Bayascas JR, Cortal M, Vidal L, Gascón P, Gómez-Ferreria M, Alfón J, Velasco G, Domènech C, Lizcano JM. The New Antitumor Drug ABTL0812 Inhibits the Akt/mTORC1 Axis by Upregulating Tribbles-3 Pseudokinase. Clin Cancer Res 2015; 22:2508-19. [PMID: 26671995 DOI: 10.1158/1078-0432.ccr-15-1808] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 11/30/2015] [Indexed: 11/16/2022]
Abstract
PURPOSE ABTL0812 is a novel first-in-class, small molecule which showed antiproliferative effect on tumor cells in phenotypic assays. Here we describe the mechanism of action of this antitumor drug, which is currently in clinical development. EXPERIMENTAL DESIGN We investigated the effect of ABTL0812 on cancer cell death, proliferation, and modulation of intracellular signaling pathways, using human lung (A549) and pancreatic (MiaPaCa-2) cancer cells and tumor xenografts. To identify cellular targets, we performed in silico high-throughput screening comparing ABTL0812 chemical structure against ChEMBL15 database. RESULTS ABTL0812 inhibited Akt/mTORC1 axis, resulting in impaired cancer cell proliferation and autophagy-mediated cell death. In silico screening led us to identify PPARs, PPARα and PPARγ as the cellular targets of ABTL0812. We showed that ABTL0812 activates both PPAR receptors, resulting in upregulation of Tribbles-3 pseudokinase (TRIB3) gene expression. Upregulated TRIB3 binds cellular Akt, preventing its activation by upstream kinases, resulting in Akt inhibition and suppression of the Akt/mTORC1 axis. Pharmacologic inhibition of PPARα/γ or TRIB3 silencing prevented ABTL0812-induced cell death. ABTL0812 treatment induced Akt inhibition in cancer cells, tumor xenografts, and peripheral blood mononuclear cells from patients enrolled in phase I/Ib first-in-human clinical trial. CONCLUSIONS ABTL0812 has a unique and novel mechanism of action, that defines a new and drugable cellular route that links PPARs to Akt/mTORC1 axis, where TRIB3 pseudokinase plays a central role. Activation of this route (PPARα/γ-TRIB3-Akt-mTORC1) leads to autophagy-mediated cancer cell death. Given the low toxicity and high tolerability of ABTL0812, our results support further development of ABTL0812 as a promising anticancer therapy. Clin Cancer Res; 22(10); 2508-19. ©2015 AACR.
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Affiliation(s)
- Tatiana Erazo
- Protein Kinases and Signal Transduction Laboratory, Institut de Neurociències and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
| | - Mar Lorente
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universidad Complutense, Madrid, Spain. Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Anna López-Plana
- Area of Molecular and Translational Oncology, IDIBAPS, Fundació Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Pau Muñoz-Guardiola
- Protein Kinases and Signal Transduction Laboratory, Institut de Neurociències and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain. Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Patricia Fernández-Nogueira
- Area of Molecular and Translational Oncology, IDIBAPS, Fundació Clínic, Universitat de Barcelona, Barcelona, Spain
| | - José A García-Martínez
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Paloma Bragado
- Area of Molecular and Translational Oncology, IDIBAPS, Fundació Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Gemma Fuster
- Area of Molecular and Translational Oncology, IDIBAPS, Fundació Clínic, Universitat de Barcelona, Barcelona, Spain
| | - María Salazar
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universidad Complutense, Madrid, Spain
| | - Jordi Espadaler
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | | | - Jose Ramon Bayascas
- Institut de Neurociències and Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marc Cortal
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Laura Vidal
- Medical Oncology Department, Novel Therapeutics Unit, Hospital Clínic Barcelona. Barcelona, Catalonia, Spain
| | - Pedro Gascón
- Area of Molecular and Translational Oncology, IDIBAPS, Fundació Clínic, Universitat de Barcelona, Barcelona, Spain. Medical Oncology Department, Novel Therapeutics Unit, Hospital Clínic Barcelona. Barcelona, Catalonia, Spain
| | - Mariana Gómez-Ferreria
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - José Alfón
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Guillermo Velasco
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universidad Complutense, Madrid, Spain. Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Carles Domènech
- Ability Pharmaceuticals, SL, Edifici Eureka, Campus UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Jose M Lizcano
- Protein Kinases and Signal Transduction Laboratory, Institut de Neurociències and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain.
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Awale M, Reymond JL. Similarity Mapplet: Interactive Visualization of the Directory of Useful Decoys and ChEMBL in High Dimensional Chemical Spaces. J Chem Inf Model 2015. [PMID: 26207526 DOI: 10.1021/acs.jcim.5b00182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An Internet portal accessible at www.gdb.unibe.ch has been set up to automatically generate color-coded similarity maps of the ChEMBL database in relation to up to two sets of active compounds taken from the enhanced Directory of Useful Decoys (eDUD), a random set of molecules, or up to two sets of user-defined reference molecules. These maps visualize the relationships between the selected compounds and ChEMBL in six different high dimensional chemical spaces, namely MQN (42-D molecular quantum numbers), SMIfp (34-D SMILES fingerprint), APfp (20-D shape fingerprint), Xfp (55-D pharmacophore fingerprint), Sfp (1024-bit substructure fingerprint), and ECfp4 (1024-bit extended connectivity fingerprint). The maps are supplied in form of Java based desktop applications called "similarity mapplets" allowing interactive content browsing and linked to a "Multifingerprint Browser for ChEMBL" (also accessible directly at www.gdb.unibe.ch ) to perform nearest neighbor searches. One can obtain six similarity mapplets of ChEMBL relative to random reference compounds, 606 similarity mapplets relative to single eDUD active sets, 30,300 similarity mapplets relative to pairs of eDUD active sets, and any number of similarity mapplets relative to user-defined reference sets to help visualize the structural diversity of compound series in drug optimization projects and their relationship to other known bioactive compounds.
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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Abstract
Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.
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Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G. Molecular fingerprint similarity search in virtual screening. Methods 2014; 71:58-63. [PMID: 25132639 DOI: 10.1016/j.ymeth.2014.08.005] [Citation(s) in RCA: 371] [Impact Index Per Article: 37.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 08/04/2014] [Accepted: 08/08/2014] [Indexed: 11/18/2022] Open
Abstract
Molecular fingerprints have been used for a long time now in drug discovery and virtual screening. Their ease of use (requiring little to no configuration) and the speed at which substructure and similarity searches can be performed with them - paired with a virtual screening performance similar to other more complex methods - is the reason for their popularity. However, there are many types of fingerprints, each representing a different aspect of the molecule, which can greatly affect search performance. This review focuses on commonly used fingerprint algorithms, their usage in virtual screening, and the software packages and online tools that provide these algorithms.
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Affiliation(s)
- Adrià Cereto-Massagué
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain
| | - María José Ojeda
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain
| | - Cristina Valls
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain
| | - Miquel Mulero
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain
| | - Santiago Garcia-Vallvé
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain; Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, CEICS, Avinguda Universitat, 1, 43204 Reus, Catalonia, Spain
| | - Gerard Pujadas
- Group of Cheminformatics & Nutrition, Biochemistry and Biotechnology Department, Universitat Rovira i Virgili (URV), Campus de Sescelades, N4 Building, 43007 Tarragona, Catalonia, Spain; Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, CEICS, Avinguda Universitat, 1, 43204 Reus, Catalonia, Spain.
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Carbonell P, Parutto P, Herisson J, Pandit SB, Faulon JL. XTMS: pathway design in an eXTended metabolic space. Nucleic Acids Res 2014; 42:W389-94. [PMID: 24792156 PMCID: PMC4086079 DOI: 10.1093/nar/gku362] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.
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Affiliation(s)
- Pablo Carbonell
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Pierre Parutto
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | - Joan Herisson
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
| | | | - Jean-Loup Faulon
- University of Evry, iSSB, F-91000 Evry, France CNRS, iSSB, F-91000 Evry, France
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Willett P. The Calculation of Molecular Structural Similarity: Principles and Practice. Mol Inform 2014; 33:403-13. [DOI: 10.1002/minf.201400024] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Accepted: 03/14/2014] [Indexed: 01/28/2023]
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Wei NN, Hamza A. SABRE: Ligand/Structure-Based Virtual Screening Approach Using Consensus Molecular-Shape Pattern Recognition. J Chem Inf Model 2013; 54:338-46. [DOI: 10.1021/ci4005496] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ning-Ning Wei
- University of Kentucky, 789 South
Limestone Street, Lexington, Kentucky 40536, United States
- ChemVS LLC, Merrick Drive, Lexington, Kentucky 40502, United States and School of life Science and Medicine, Dalian University of Technology, Panjin, LN 124221, China
| | - Adel Hamza
- University of Kentucky, 789 South
Limestone Street, Lexington, Kentucky 40536, United States
- ChemVS LLC, Merrick Drive, Lexington, Kentucky 40502, United States and School of life Science and Medicine, Dalian University of Technology, Panjin, LN 124221, China
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Santos RG, Giulianotti MA, Houghten RA, Medina-Franco JL. Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. J Chem Inf Model 2013; 53:2613-25. [PMID: 23971977 PMCID: PMC3850180 DOI: 10.1021/ci400243e] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Structure-property relationships and structure-activity relationships play an important role in many research areas, such as medicinal chemistry and drug discovery. Such methods, however, have focused on providing post-hoc descriptions of such relationships based on known data. The ability for these descriptions to remain relevant when considering compounds of unknown activity, and thus the prediction of activity and property landscapes using existing data, remains little explored. In this study, we present a novel method of evaluating the ability of a compound comparison methodology to provide accurate information about a set of unknown compounds and also explore the ability of these predicted activity landscapes to prioritize active compounds over inactive. These methods are applied to three distinct and diverse sets of compounds, each with activity data for multiple targets, for a total of eight target-compound set pairs. Six methodologically distinct compound comparison methods were evaluated. We show that overall, all compound comparison methods provided an improvement in structure-activity relationship prediction over random and were able to prioritize compounds in a superior manner to random sampling, but the degree of success and therefore applicability varied markedly.
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Affiliation(s)
- Radleigh G. Santos
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - Marc A. Giulianotti
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - Richard A. Houghten
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987
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Tebby C, Mombelli E. Modelling Structure Activity Landscapes with Cliffs: a Kernel Regression-Based Approach. Mol Inform 2013; 32:609-23. [DOI: 10.1002/minf.201300016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 04/19/2013] [Indexed: 11/08/2022]
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Koch U, Hamacher M, Nussbaumer P. Cheminformatics at the interface of medicinal chemistry and proteomics. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:156-61. [PMID: 23707564 DOI: 10.1016/j.bbapap.2013.05.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Revised: 04/26/2013] [Accepted: 05/13/2013] [Indexed: 10/26/2022]
Abstract
Multiple factors have to be optimized in the course of a drug discovery project. Traditionally this includes potency on a single target, eventually specificity as well as the pharmacokinetic, physicochemical and the safety profile. Recently an additional dimension has been added by realizing that the therapeutic outcome of a drug is often determined not only by its activity on a single target but also by its activity profile across a variety of biological targets. To address the polypharmacology of drug candidates many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. To extract useful information computational methods at the interface of proteomics and cheminformatics are indispensable. This review will focus on some recent developments in this field. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Affiliation(s)
- Uwe Koch
- Lead Discovery Center GmbH, Otto-Hahn-Str. 15, D-44227 Dortmund, Germany.
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Menikarachchi LC, Hamdalla MA, Hill DW, Grant DF. Chemical structure identification in metabolomics: computational modeling of experimental features. Comput Struct Biotechnol J 2013; 5:e201302005. [PMID: 24688698 PMCID: PMC3962140 DOI: 10.5936/csbj.201302005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Revised: 12/20/2012] [Accepted: 12/24/2012] [Indexed: 11/30/2022] Open
Abstract
The identification of compounds in complex mixtures remains challenging despite recent advances in analytical techniques. At present, no single method can detect and quantify the vast array of compounds that might be of potential interest in metabolomics studies. High performance liquid chromatography/mass spectrometry (HPLC/MS) is often considered the analytical method of choice for analysis of biofluids. The positive identification of an unknown involves matching at least two orthogonal HPLC/MS measurements (exact mass, retention index, drift time etc.) against an authentic standard. However, due to the limited availability of authentic standards, an alternative approach involves matching known and measured features of the unknown compound with computationally predicted features for a set of candidate compounds downloaded from a chemical database. Computationally predicted features include retention index, ECOM50 (energy required to decompose 50% of a selected precursor ion in a collision induced dissociation cell), drift time, whether the unknown compound is biological or synthetic and a collision induced dissociation (CID) spectrum. Computational predictions are used to filter the initial “bin” of candidate compounds. The final output is a ranked list of candidates that best match the known and measured features. In this mini review, we discuss cheminformatics methods underlying this database search-filter identification approach.
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Affiliation(s)
- Lochana C Menikarachchi
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, United States
| | - Mai A Hamdalla
- Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Road, Unit 2155 Storrs, CT 06269, United States
| | - Dennis W Hill
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, United States
| | - David F Grant
- Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, United States
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Hamdalla MA, Mandoiu II, Hill DW, Rajasekaran S, Grant DF. BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. J Chem Inf Model 2013; 53:601-12. [PMID: 23330685 DOI: 10.1021/ci300512q] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration. In this paper we present BioSM; a cheminformatics tool that uses known endogenous mammalian biochemical compounds (as scaffolds) and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSM identifies endogenous mammalian biochemical structures with high accuracy. In a leave-one-out cross validation experiment, BioSM correctly predicted 95% of 1388 Kyoto Encyclopedia of Genes and Genomes (KEGG) compounds as endogenous mammalian biochemicals using 1565 scaffolds. Analysis of two additional biological data sets containing 2330 human metabolites (HMDB) and 2416 plant secondary metabolites (KEGG) resulted in biochemical annotations of 89% and 72% of the compounds, respectively. When a data set of 3895 drugs (DrugBank and USAN) was tested, 48% of these structures were predicted to be biochemical. However, when a set of synthetic chemical compounds (Chembridge and Chemsynthesis databases) were examined, only 29% of the 458,207 structures were predicted to be biochemical. Moreover, BioSM predicted that 34% of 883,199 randomly selected compounds from PubChem were biochemical. We then expanded the scaffold list to 3927 biochemical compounds and reevaluated the above data sets to determine whether scaffold number influenced model performance. Although there were significant improvements in model sensitivity and specificity using the larger scaffold list, the data set comparison results were very similar. These results suggest that additional biochemical scaffolds will not further improve our representation of biochemical structure space and that the model is reasonably robust. BioSM provides a qualitative (yes/no) and quantitative (ranking) method for endogenous mammalian biochemical annotation of chemical space and, thus, will be useful in the identification of unknown biochemical structures in metabolomics. BioSM is freely available at http://metabolomics.pharm.uconn.edu.
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Affiliation(s)
- Mai A Hamdalla
- Computer Science and Engineering Department and ‡Pharmaceutical Sciences Department, University of Connecticut , Connecticut, United States
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41
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Affiliation(s)
- Peter Willett
- Information School, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, United Kingdom.
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Seal A, Yogeeswari P, Sriram D, Wild DJ. Enhanced ranking of PknB Inhibitors using data fusion methods. J Cheminform 2013; 5:2. [PMID: 23317154 PMCID: PMC3600029 DOI: 10.1186/1758-2946-5-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Accepted: 12/11/2012] [Indexed: 11/18/2022] Open
Abstract
Background Mycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB. Results In our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation. Conclusion We show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening.
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Affiliation(s)
- Abhik Seal
- Computer-Aided Drug Design Laboratory, Department of Pharmacy Birla Institute of Technology, Hyderabad Campus, Shameerpet, Hyderbad, 500078, India.
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Méndez-Lucio O, Pérez-Villanueva J, Castillo R, Medina-Franco JL. Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps. Mol Inform 2012; 31:837-46. [DOI: 10.1002/minf.201200078] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2012] [Accepted: 10/06/2012] [Indexed: 01/27/2023]
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Medina-Franco JL. Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. J Chem Inf Model 2012; 52:2485-93. [PMID: 22989212 DOI: 10.1021/ci300362x] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Systematic description of structure-activity relationships (SARs) of data sets and structure-property relationships (SPRs) is of paramount importance in medicinal chemistry and other research fields. To this end, structure-activity similarity (SAS) maps are one of the first tools proposed to describe SARs using the concept of activity landscape modeling. One of the major goals of the SAS maps is to identify activity cliffs defined as chemical compounds with high similar structure but unexpectedly very different biological activity. Since the first publication of the SAS maps more than ten years ago, these tools have evolved and adapted over the years to analyze various types of compound collections, including structural diverse and combinatorial sets with activity for one or multiple biological end points. The development of SAS maps has led to general concepts that are applicable to other activity landscape methods such as "consensus activity cliffs" (activity cliffs common to a series of representations or descriptors) and "selectivity switches" (structural changes that completely invert the selectivity pattern of similar compounds against two biological end points). Herein, we review the development, practical applications, limitations, and perspectives of the SAS and related maps which are intuitive and powerful informatics tools to computationally analyze SPRs.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, USA.
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Kraft R, Kahn A, Medina-Franco JL, Orlowski ML, Baynes C, López-Vallejo F, Barnard K, Maggiora GM, Restifo LL. A cell-based fascin bioassay identifies compounds with potential anti-metastasis or cognition-enhancing functions. Dis Model Mech 2012; 6:217-35. [PMID: 22917928 PMCID: PMC3529353 DOI: 10.1242/dmm.008243] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The actin-bundling protein fascin is a key mediator of tumor invasion and metastasis and its activity drives filopodia formation, cell-shape changes and cell migration. Small-molecule inhibitors of fascin block tumor metastasis in animal models. Conversely, fascin deficiency might underlie the pathogenesis of some developmental brain disorders. To identify fascin-pathway modulators we devised a cell-based assay for fascin function and used it in a bidirectional drug screen. The screen utilized cultured fascin-deficient mutant Drosophila neurons, whose neurite arbors manifest the 'filagree' phenotype. Taking a repurposing approach, we screened a library of 1040 known compounds, many of them FDA-approved drugs, for filagree modifiers. Based on scaffold distribution, molecular-fingerprint similarities, and chemical-space distribution, this library has high structural diversity, supporting its utility as a screening tool. We identified 34 fascin-pathway blockers (with potential anti-metastasis activity) and 48 fascin-pathway enhancers (with potential cognitive-enhancer activity). The structural diversity of the active compounds suggests multiple molecular targets. Comparisons of active and inactive compounds provided preliminary structure-activity relationship information. The screen also revealed diverse neurotoxic effects of other drugs, notably the 'beads-on-a-string' defect, which is induced solely by statins. Statin-induced neurotoxicity is enhanced by fascin deficiency. In summary, we provide evidence that primary neuron culture using a genetic model organism can be valuable for early-stage drug discovery and developmental neurotoxicity testing. Furthermore, we propose that, given an appropriate assay for target-pathway function, bidirectional screening for brain-development disorders and invasive cancers represents an efficient, multipurpose strategy for drug discovery.
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Affiliation(s)
- Robert Kraft
- Department of Neuroscience, University of Arizona, Tucson, AZ 85721, USA
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Yongye AB, Medina-Franco JL. Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds. J Chem Inf Model 2012; 52:2454-61. [PMID: 22856455 DOI: 10.1021/ci3002606] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure-activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data.
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Affiliation(s)
- Austin B Yongye
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, USA
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Medina-Franco JL, Yongye AB, Pérez-Villanueva J, Houghten RA, Martínez-Mayorga K. Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. J Chem Inf Model 2011; 51:2427-39. [PMID: 21842860 DOI: 10.1021/ci200281v] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, United States.
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Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision. J Cheminform 2011; 3:29. [PMID: 21824430 PMCID: PMC3195112 DOI: 10.1186/1758-2946-3-29] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Accepted: 08/08/2011] [Indexed: 11/28/2022] Open
Abstract
Background Data fusion methods are widely used in virtual screening, and make the implicit assumption that the more often a molecule is retrieved in multiple similarity searches, the more likely it is to be active. This paper tests the correctness of this assumption. Results Sets of 25 searches using either the same reference structure and 25 different similarity measures (similarity fusion) or 25 different reference structures and the same similarity measure (group fusion) show that large numbers of unique molecules are retrieved by just a single search, but that the numbers of unique molecules decrease very rapidly as more searches are considered. This rapid decrease is accompanied by a rapid increase in the fraction of those retrieved molecules that are active. There is an approximately log-log relationship between the numbers of different molecules retrieved and the number of searches carried out, and a rationale for this power-law behaviour is provided. Conclusions Using multiple searches provides a simple way of increasing the precision of a similarity search, and thus provides a justification for the use of data fusion methods in virtual screening.
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Yongye AB, Byler K, Santos R, Martínez-Mayorga K, Maggiora GM, Medina-Franco JL. Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations. J Chem Inf Model 2011; 51:1259-70. [DOI: 10.1021/ci200081k] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Austin B. Yongye
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Kendall Byler
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Radleigh Santos
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Karina Martínez-Mayorga
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Gerald M. Maggiora
- Department of Pharmacology & Toxicology, University of Arizona College of Pharmacy, 1703 E. Mabel Street, Tucson, Arizona 85721, United States and Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, Arizona 85004, United States
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
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