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Amoroso N, Gambacorta N, Mastrolorito F, Togo MV, Trisciuzzi D, Monaco A, Pantaleo E, Altomare CD, Ciriaco F, Nicolotti O. Making sense of chemical space network shows signs of criticality. Sci Rep 2023; 13:21335. [PMID: 38049451 PMCID: PMC10696027 DOI: 10.1038/s41598-023-48107-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy.
| | - Nicola Gambacorta
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
- Division of Medical Genetics, Fondazione IRCCS-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
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Lungu CN, Mangalagiu V, Mangalagiu II, Mehedinti MC. Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design. Molecules 2023; 28:molecules28031069. [PMID: 36770739 PMCID: PMC9921191 DOI: 10.3390/molecules28031069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/09/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.
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Affiliation(s)
- Claudiu N. Lungu
- Department of Surgery, Emergency Country Clinical Hospital, 800010 Galati, Romania
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Department of Morphological and Functional Science, University of Medicine and Pharmacy, Dunarea de Jos, 800017 Galati, Romania
- Correspondence: (C.N.L.); (I.I.M.)
| | - Violeta Mangalagiu
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Faculty of Food Engineering, Stefan cel Mare University of Suceava, 13 Universitatii Str., 720229 Suceava, Romania
| | - Ionel I. Mangalagiu
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Institute of Interdisciplinary Research-CERNESIM Centre, Alexandru Ioan Cuza University of Iasi, 11 Carol I, 700506 Iasi, Romania
- Correspondence: (C.N.L.); (I.I.M.)
| | - Mihaela C. Mehedinti
- Faculty of Chemistry, Alexandru Ioan Cuza University of Iasi, 11 Carol 1st Bvd, 700506 Iasi, Romania
- Department of Morphological and Functional Science, University of Medicine and Pharmacy, Dunarea de Jos, 800017 Galati, Romania
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Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy. NPJ Syst Biol Appl 2021; 7:11. [PMID: 33589646 PMCID: PMC7884732 DOI: 10.1038/s41540-021-00171-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/13/2021] [Indexed: 01/31/2023] Open
Abstract
Advancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from "one-target, one-drug" to "target-network, multi-component therapeutics". We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein-protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.
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Huang Y, Xie Y, Zhong C, Zhou F. Finding branched pathways in metabolic network via atom group tracking. PLoS Comput Biol 2021; 17:e1008676. [PMID: 33529200 PMCID: PMC7880430 DOI: 10.1371/journal.pcbi.1008676] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 02/12/2021] [Accepted: 01/05/2021] [Indexed: 12/27/2022] Open
Abstract
Finding non-standard or new metabolic pathways has important applications in metabolic engineering, synthetic biology and the analysis and reconstruction of metabolic networks. Branched metabolic pathways dominate in metabolic networks and depict a more comprehensive picture of metabolism compared to linear pathways. Although progress has been developed to find branched metabolic pathways, few efforts have been made in identifying branched metabolic pathways via atom group tracking. In this paper, we present a pathfinding method called BPFinder for finding branched metabolic pathways by atom group tracking, which aims to guide the synthetic design of metabolic pathways. BPFinder enumerates linear metabolic pathways by tracking the movements of atom groups in metabolic network and merges the linear atom group conserving pathways into branched pathways. Two merging rules based on the structure of conserved atom groups are proposed to accurately merge the branched compounds of linear pathways to identify branched pathways. Furthermore, the integrated information of compound similarity, thermodynamic feasibility and conserved atom groups is also used to rank the pathfinding results for feasible branched pathways. Experimental results show that BPFinder is more capable of recovering known branched metabolic pathways as compared to other existing methods, and is able to return biologically relevant branched pathways and discover alternative branched pathways of biochemical interest. The online server of BPFinder is available at http://114.215.129.245:8080/atomic/. The program, source code and data can be downloaded from https://github.com/hyr0771/BPFinder. Computational search of branched metabolic pathways is a fundamental problem in metabolic engineering and metabolic network analysis, which provides a systematic way of understanding the metabolism and discovering alternative pathways for synthesis of useful biomolecules. We propose BPFinder, a novel computational approach to identify branched metabolic pathways via atom group tracking. Different from other pathfinding methods using atom tracking, BPFinder tracks the movement of atom groups in metabolic network to find linear atom group conserving pathways, and merge the found linear pathways by the selected branched compounds to generate branched pathways. Based on the structure of conserved atom groups in branched compounds, we design two merging rules for branched compounds: overlapping rule and non-overlapping rule. The user can flexibly adopt these rules to accurately find the branched pathways that contain overlapping/non-overlapping conserved atom groups. BPFinder also enables the user to combine the information of compound similarity, Gibbs free energy of reactions, and conserved atom groups to sort resulting pathways. Compared with other existing methods, BPFinder can more accurately recover the known branched pathways. The alternative branched pathways returned by BPFinder reveal that the user can flexibly utilize our proposed merging rules to discover biochemically meaningful pathways of interest.
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Affiliation(s)
- Yiran Huang
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
- * E-mail:
| | - Yusi Xie
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
| | - Cheng Zhong
- School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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5
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Schüler JA, Rechner S, Müller-Hannemann M. MET: a Java package for fast molecule equivalence testing. J Cheminform 2020; 12:73. [PMID: 33334379 PMCID: PMC7745470 DOI: 10.1186/s13321-020-00480-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 12/03/2020] [Indexed: 11/11/2022] Open
Abstract
An important task in cheminformatics is to test whether two molecules are equivalent with respect to their 2D structure. Mathematically, this amounts to solving the graph isomorphism problem for labelled graphs. In this paper, we present an approach which exploits chemical properties and the local neighbourhood of atoms to define highly distinctive node labels. These characteristic labels are the key for clever partitioning molecules into molecule equivalence classes and an effective equivalence test. Based on extensive computational experiments, we show that our algorithm is significantly faster than existing implementations within SMSD, CDK and RDKit. We provide our Java implementation as an easy-to-use, open-source package (via GitHub) which is compatible with CDK. It fully supports the distinction of different isotopes and molecules with radicals.
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Affiliation(s)
- Jördis-Ann Schüler
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle, Germany.
| | - Steffen Rechner
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle, Germany
| | - Matthias Müller-Hannemann
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle, Germany
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6
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Antelo-Collado A, Carrasco-Velar R, García-Pedrajas N, Cerruela-García G. Maximum common property: a new approach for molecular similarity. J Cheminform 2020; 12:61. [PMID: 33372638 PMCID: PMC7547443 DOI: 10.1186/s13321-020-00462-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 09/14/2020] [Indexed: 12/02/2022] Open
Abstract
The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches. ![]()
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Affiliation(s)
- Aurelio Antelo-Collado
- University of Informatics Science, Carretera San Antonio de los Baños Km. 2 1/2 , Boyeros, La Habana, Cuba, Havana, Cuba
| | - Ramón Carrasco-Velar
- University of Informatics Science, Carretera San Antonio de los Baños Km. 2 1/2 , Boyeros, La Habana, Cuba, Havana, Cuba.
| | - Nicolás García-Pedrajas
- Department of Computing and Numerical Analysis, University of Cordoba, Campus de Rabanales, Albert Einstein Building, E-14071, Córdoba, Spain
| | - Gonzalo Cerruela-García
- Department of Computing and Numerical Analysis, University of Cordoba, Campus de Rabanales, Albert Einstein Building, E-14071, Córdoba, Spain
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7
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Li HL, Song XM, Wu F, Qiu YL, Fu XB, Zhang LY, Tan J. Chemical structure of semiochemicals and key binding sites together determine the olfactory functional modes of odorant-binding protein 2 in Eastern honey bee, Apis cerana. Int J Biol Macromol 2020; 145:876-884. [DOI: 10.1016/j.ijbiomac.2019.11.189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/21/2019] [Accepted: 11/21/2019] [Indexed: 11/24/2022]
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8
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Computational analysis of drug like candidates against Neuraminidase of Human Influenza A virus subtypes. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100284] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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9
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Holliday GL, Brown SD, Mischel D, Polacco BJ, Babbitt PC. A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function. Database (Oxford) 2020; 2020:baaa034. [PMID: 32449511 PMCID: PMC7246345 DOI: 10.1093/database/baaa034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 03/18/2020] [Accepted: 04/27/2020] [Indexed: 12/12/2022]
Abstract
Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how' these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).
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Affiliation(s)
- Gemma L Holliday
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
- Present Address: Medicines Discovery Catapult, Mereside, Alderley Park, Alderley Edge SK10 4TG, UK
| | - Shoshana D Brown
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
| | - David Mischel
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
| | - Benjamin J Polacco
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
| | - Patricia C Babbitt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, 1700 4th Street, CA 94143, USA
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10
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Son B, Lee S, Kim H, Kang H, Jeon J, Jo S, Seong KM, Lee SJ, Youn H, Youn B. Decreased FBP1 expression rewires metabolic processes affecting aggressiveness of glioblastoma. Oncogene 2019; 39:36-49. [PMID: 31444412 DOI: 10.1038/s41388-019-0974-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 06/11/2019] [Accepted: 06/15/2019] [Indexed: 12/14/2022]
Abstract
Radiotherapy is a standard treatment option for patients with glioblastoma (GBM). Although it has high therapeutic efficacy, some proportion of the tumor cells that survive after radiotherapy may cause side effects. In this study, we found that fructose 1,6-bisphosphatase 1 (FBP1), a rate-limiting enzyme in gluconeogenesis, was downregulated upon treatment with ionizing radiation (IR). Ets1, which was found to be overexpressed in IR-induced infiltrating GBM, was suggested to be a transcriptional repressor of FBP1. Furthermore, glucose uptake and extracellular acidification rates were increased upon FBP1 downregulation, which indicated an elevated glycolysis level. We found that emodin, an inhibitor of phosphoglycerate mutase 1 derived from natural substances, significantly suppressed the glycolysis rate and IR-induced GBM migration in in vivo orthotopic xenograft mouse models. We propose that the reduced FBP1 level reprogrammed the metabolic state of GBM cells, and thus, FBP1 is a potential therapeutic target regulating GBM metabolism following radiotherapy.
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Affiliation(s)
- Beomseok Son
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea
| | - Sungmin Lee
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea
| | - Hyunwoo Kim
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea
| | - Hyunkoo Kang
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea
| | - Jaewan Jeon
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea.,Department of Radiation Oncology, Haeundae Paik Hospital, Inje University School of Medicine, Busan, 48108, Republic of Korea
| | - Sunmi Jo
- Department of Radiation Oncology, Haeundae Paik Hospital, Inje University School of Medicine, Busan, 48108, Republic of Korea
| | - Ki Moon Seong
- Laboratory of Low Dose Risk Assessment, National Radiation Emergency Medical Center, Korea Institute of Radiological & Medical Sciences, Seoul, 01812, Republic of Korea
| | - Su-Jae Lee
- Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | - HyeSook Youn
- Department of Integrative Bioscience and Biotechnology, Sejong University, Seoul, 05006, Republic of Korea
| | - BuHyun Youn
- Department of Integrated Biological Science, Pusan National University, Busan, 46241, Republic of Korea. .,Department of Biological Sciences, Pusan National University, Busan, 46241, Republic of Korea.
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Zhou C, Ieritano C, Hopkins WS. Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility. Front Chem 2019; 7:519. [PMID: 31440497 PMCID: PMC6693329 DOI: 10.3389/fchem.2019.00519] [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: 04/30/2019] [Accepted: 07/08/2019] [Indexed: 12/17/2022] Open
Abstract
Evolutionary algorithms such as the basin-hopping (BH) algorithm have proven to be useful for difficult non-linear optimization problems with multiple modalities and variables. Applications of these algorithms range from characterization of molecular states in statistical physics and molecular biology to geometric packing problems. A key feature of BH is the fact that one can generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima. These results can then be utilized to gain insights into molecular dynamics and thermodynamic properties. Here we describe how one can employ concepts from unsupervised machine learning to augment BH PES searches to more efficiently identify local minima and the transition states connecting them. Specifically, we introduce the concepts of similarity indices, hierarchical clustering, and multidimensional scaling to the BH methodology. These same machine learning techniques can be used as tools for interpreting and rationalizing experimental results from spectroscopic and ion mobility investigations (e.g., spectral assignment, dynamic collision cross sections). We exemplify this in two case studies: (1) assigning the infrared multiple photon dissociation spectrum of the protonated serine dimer and (2) determining the temperature-dependent collision cross-section of protonated alanine tripeptide.
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Affiliation(s)
- Ce Zhou
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
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12
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Tyzack JD, Furnham N, Sillitoe I, Orengo CM, Thornton JM. Exploring Enzyme Evolution from Changes in Sequence, Structure, and Function. Methods Mol Biol 2019; 1851:263-275. [PMID: 30298402 DOI: 10.1007/978-1-4939-8736-8_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The goal of our research is to increase our understanding of how biology works at the molecular level, with a particular focus on how enzymes evolve their functions through adaptations to generate new specificities and mechanisms. FunTree (Sillitoe and Furnham, Nucleic Acids Res 44:D317-D323, 2016) is a resource that brings together sequence, structure, phylogenetic, and chemical and mechanistic information for 2340 CATH superfamilies (Sillitoe et al., Nucleic Acids Res 43:D376-D381, 2015) (which all contain at least one enzyme) to allow evolution to be investigated within a structurally defined superfamily.We will give an overview of FunTree's use of sequence and structural alignments to cluster proteins within a superfamily into structurally similar groups (SSGs) and generate phylogenetic trees augmented by ancestral character estimations (ACE). This core information is supplemented with new measures of functional similarity (Rahman et al., Nat Methods 11:171-174, 2014) to compare enzyme reactions based on overall bond changes, reaction centers (the local environment atoms involved in the reaction), and the structural similarities of the metabolites involved in the reaction. These trees are also decorated with taxonomic and Enzyme Commission (EC) code and GO annotations, forming the basis of a comprehensive web interface that can be found at http://www.funtree.info . In this chapter, we will discuss the various analyses and supporting computational tools in more detail, describing the steps required to extract information.
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Affiliation(s)
| | | | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Christine M Orengo
- Institute of Structural and Molecular Biology, University College London, London, UK
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Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents. PLoS Comput Biol 2018; 14:e1006515. [PMID: 30346968 PMCID: PMC6211772 DOI: 10.1371/journal.pcbi.1006515] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 11/01/2018] [Accepted: 09/15/2018] [Indexed: 01/31/2023] Open
Abstract
The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite’s biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease. The rise of resistance through the intensive use of drugs targeted to treat specific infectious diseases means that new therapeutics are continually required. Diseases common in the tropics and sub-tropics, classified as neglected tropical diseases, suffer from a lack of new drug treatments due to the difficulty in developing new drugs and the lack of market incentive. One such disease is schistosomiasis, a major human helminth disease caused by worms from the genus Schistosoma. It is currently treated by a 40-year old drug, praziquantel, but its widespread use has led to signs of drug-resistance emerging, with no alternative effective treatments available. To meet this challenge, we have developed an innovative computational drug repurposing pipeline supported by experimental phenotypic screening. Protein kinases emerged from our pipeline as interesting new biological targets. Given that many human kinase inhibitors have been successfully applied specially in cancer therapy and kinases have conserved structures and functions, we also undertook a detailed analysis of the kinases present in all infective Schistosoma species and human host. This allowed identification of new Schistosoma-specific kinase targets and suggest approved drugs to be used for treating schistosomiasis as well as opening new avenues to treat this neglected disease.
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Wang C, Ren Q, Chen XT, Song ZQ, Ning ZC, Gan JH, Ma XL, Liang DR, Guan DG, Liu ZL, Lu AP. System Pharmacology-Based Strategy to Decode the Synergistic Mechanism of Zhi-zhu Wan for Functional Dyspepsia. Front Pharmacol 2018; 9:841. [PMID: 30127739 PMCID: PMC6087764 DOI: 10.3389/fphar.2018.00841] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 07/12/2018] [Indexed: 12/12/2022] Open
Abstract
Functional dyspepsia (FD) is a widely prevalent gastrointestinal disorder throughout the world, whereas the efficacy of current treatment in the Western countries is limited. As the symptom is equivalent to the traditional Chinese medicine (TCM) term "stuffiness and fullness," FD can be treated with Zhi-zhu Wan (ZZW) which is a kind of Chinese patent medicine. However, the "multi-component" and "multi-target" feature of Chinese patent medicine makes it challenge to elucidate the potential therapeutic mechanisms of ZZW on FD. Presently, a novel system pharmacology model including pharmacokinetic parameters, pharmacological data, and component contribution score (CS) is constructed to decipher the potential therapeutic mechanism of ZZW on FD. Finally, 61 components with favorable pharmacokinetic profiles and biological activities were obtained through ADME (absorption, distribution, metabolism, and excretion) screening in silico. The related targets of these components are identified by component targeting process followed by GO analysis and pathway enrichment analysis. And systematic analysis found that through acting on the target related to inflammation, gastrointestinal peristalsis, and mental disorder, ZZW plays a synergistic and complementary effect on FD at the pathway level. Furthermore, the component CS showed that 29 components contributed 90.18% of the total CS values of ZZW for the FD treatment, which suggested that the effective therapeutic effects of ZZW for FD are derived from all active components, not a few components. This study proposes the system pharmacology method and discovers the potent combination therapeutic mechanisms of ZZW for FD. This strategy will provide a reference method for other TCM mechanism research.
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Affiliation(s)
- Chun Wang
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Qing Ren
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Xue-Tong Chen
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
- Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China
| | - Zhi-Qian Song
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhang-Chi Ning
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Jia-He Gan
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xin-Ling Ma
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dong-Rui Liang
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dao-Gang Guan
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Zhen-Li Liu
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ai-Ping Lu
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong
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Noh K, Yoo S, Lee D. A systematic approach to identify therapeutic effects of natural products based on human metabolite information. BMC Bioinformatics 2018; 19:205. [PMID: 29897322 PMCID: PMC5998763 DOI: 10.1186/s12859-018-2196-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Natural products have been widely investigated in the drug development field. Their traditional use cases as medicinal agents and their resemblance of our endogenous compounds show the possibility of new drug development. Many researchers have focused on identifying therapeutic effects of natural products, yet the resemblance of natural products and human metabolites has been rarely touched. METHODS We propose a novel method which predicts therapeutic effects of natural products based on their similarity with human metabolites. In this study, we compare the structure, target and phenotype similarities between natural products and human metabolites to capture molecular and phenotypic properties of both compounds. With the generated similarity features, we train support vector machine model to identify similar natural product and human metabolite pairs. The known functions of human metabolites are then mapped to the paired natural products to predict their therapeutic effects. RESULTS With our selected three feature sets, structure, target and phenotype similarities, our trained model successfully paired similar natural products and human metabolites. When applied to the natural product derived drugs, we could successfully identify their indications with high specificity and sensitivity. We further validated the found therapeutic effects of natural products with the literature evidence. CONCLUSIONS These results suggest that our model can match natural products to similar human metabolites and provide possible therapeutic effects of natural products. By utilizing the similar human metabolite information, we expect to find new indications of natural products which could not be covered by previous in silico methods.
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Affiliation(s)
- Kyungrin Noh
- Bio-Synergy Research Center, Daejeon, 34141, South Korea
| | - Sunyong Yoo
- Bio-Synergy Research Center, Daejeon, 34141, South Korea.,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Doheon Lee
- Bio-Synergy Research Center, Daejeon, 34141, South Korea. .,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
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16
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Suphavilai C, Bertrand D, Nagarajan N. Predicting Cancer Drug Response using a Recommender System. Bioinformatics 2018; 34:3907-3914. [DOI: 10.1093/bioinformatics/bty452] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 05/31/2018] [Indexed: 01/10/2023] Open
Affiliation(s)
- Chayaporn Suphavilai
- Department of Computer Science, School of Computing, National University of Singapore, Singapore
- Computational and Systems Biology, Genome Institute of Singapore, Singapore
| | - Denis Bertrand
- Computational and Systems Biology, Genome Institute of Singapore, Singapore
| | - Niranjan Nagarajan
- Computational and Systems Biology, Genome Institute of Singapore, Singapore
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17
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Wang J, Meng F, Dai E, Yang F, Wang S, Chen X, Yang L, Wang Y, Jiang W. Identification of associations between small molecule drugs and miRNAs based on functional similarity. Oncotarget 2018; 7:38658-38669. [PMID: 27232942 PMCID: PMC5122418 DOI: 10.18632/oncotarget.9577] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 05/08/2016] [Indexed: 12/18/2022] Open
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that regulate gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in varieties of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapy for many human diseases, especially cancer. Here, we proposed a novel computational approach to identify associations between small molecules and miRNAs based on functional similarity of differentially expressed genes. At the significance level of p < 0.01, we constructed the small molecule and miRNA functional similarity network involving 111 small molecules and 20 miRNAs. Moreover, we also predicted associations between drugs and diseases through integrating our identified small molecule-miRNA associations with experimentally validated disease related miRNAs. As a result, we identified 2265 associations between FDA approved drugs and diseases, in which ~35% associations have been validated by comprehensive literature reviews. For breast cancer, we identified 19 potential drugs, in which 12 drugs were supported by previous studies. In addition, we performed survival analysis for the patients from TCGA and GEO database, which indicated that the associated miRNAs of 4 drugs might be good prognosis markers in breast cancer. Collectively, this study proposed a novel approach to predict small molecule and miRNA associations based on functional similarity, which may pave a new way for miRNA-targeted therapy and drug repositioning.
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Affiliation(s)
- Jing Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Fanlin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - EnYu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Yuwen Wang
- The 2nd Affiliated Hospital, Harbin Medical University, Harbin 150081, P. R. China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
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Duesbury E, Holliday J, Willett P. Comparison of Maximum Common Subgraph Isomorphism Algorithms for the Alignment of 2D Chemical Structures. ChemMedChem 2017; 13:588-598. [DOI: 10.1002/cmdc.201700482] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Revised: 10/10/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Edmund Duesbury
- Computer-Aided Drug Design; UCB Pharma; 208 Bath Road Slough SL1 3WE UK
- Information School; University of Sheffield; 211 Portobello Sheffield S1 4DP UK
| | - John Holliday
- 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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Abstract
The newer atypical antipsychotic agents (AAPs) represent an attractive therapeutic option for a wide range of psychotic disorders, including schizophrenia and bipolar mania, because of the reduced risk of disabling extrapyramidal symptoms. However, their growing use has raised questions about their tolerability over the endocrine, metabolic, and cardiovascular axes. Indeed, atypical antipsychotic drugs are associated, to differing extents, with mild elevation of aminotransferases related to weight gain, AAP-induced metabolic syndrome, and nonalcoholic fatty liver disease. Although the hepatic safety of new AAPs seems improved over that of chlorpromazine, they can occasionally cause idiosyncratic liver injury with varying phenotypes and, rarely, lead to acute liver failure. However, AAPs are a group of heterogeneous, chemically unrelated compounds with distinct pharmacological and pharmacokinetic properties and substantially different safety profiles, which precludes the notion of a class effect for hepatotoxicity risk and highlights the need for an individualized therapeutic approach. We discuss the current evidence on the hepatotoxicity potential of AAPs, the emerging underlying mechanisms, and the limitations inherent to this group of drugs for both establishing a proper causality assessment and developing strategies for risk management.
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20
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Sankar A, Ranu S, Raman K. Predicting novel metabolic pathways through subgraph mining. Bioinformatics 2017; 33:3955-3963. [DOI: 10.1093/bioinformatics/btx481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aravind Sankar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Sayan Ranu
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
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21
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Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 2017; 9:33. [PMID: 29086040 PMCID: PMC5461230 DOI: 10.1186/s13321-017-0220-4] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 05/16/2017] [Indexed: 12/15/2022] Open
Abstract
Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.CDK 2.0 provides new features and improved performance ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0220-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands.
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Arvid Berg
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Lars Carlsson
- AstraZeneca, Innovative Medicines & Early Development, Quantitative Biology, Möndal, Sweden
| | | | - Stefan Kuhn
- Department of Informatics, University of Leicester, Leicester, UK
| | - Tomáš Pluskal
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | | | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Lessingstr. 8, 07743, Jena, Germany
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22
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A polygraph test for trustworthy structural similarity. INFORM SYST 2017. [DOI: 10.1016/j.is.2016.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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Huang Y, Zhong C, Lin HX, Wang J. A Method for Finding Metabolic Pathways Using Atomic Group Tracking. PLoS One 2017; 12:e0168725. [PMID: 28068354 PMCID: PMC5221824 DOI: 10.1371/journal.pone.0168725] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 12/05/2016] [Indexed: 12/13/2022] Open
Abstract
A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.
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Affiliation(s)
- Yiran Huang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
- * E-mail: (YH); (CZ)
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
- * E-mail: (YH); (CZ)
| | - Hai Xiang Lin
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jianyi Wang
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning, China
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24
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Štular T, Lešnik S, Rožman K, Schink J, Zdouc M, Ghysels A, Liu F, Aldrich CC, Haupt VJ, Salentin S, Daminelli S, Schroeder M, Langer T, Gobec S, Janežič D, Konc J. Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction. J Med Chem 2016; 59:11069-11078. [PMID: 27936766 DOI: 10.1021/acs.jmedchem.6b01277] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.
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Affiliation(s)
- Tanja Štular
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Kaja Rožman
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Julia Schink
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Mitja Zdouc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - An Ghysels
- Center for Molecular Modeling, Ghent University , Technologiepark 903, 9052 Zwijnaarde, Belgium
| | - Feng Liu
- AAT Bioquest, Inc. , 520 Mercury Drive, Sunnyvale, California 94085, United States
| | - Courtney C Aldrich
- Department of Medicinal Chemistry, University of Minnesota , 308 Harvard Street Southeast, Minneapolis, Minnesota 55455, United States
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Sebastian Salentin
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Simone Daminelli
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstrasse 14, A-1090 Vienna, Austria
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Janez Konc
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
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ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 2016; 8:61. [PMID: 27867422 PMCID: PMC5096306 DOI: 10.1186/s13321-016-0174-y] [Citation(s) in RCA: 602] [Impact Index Per Article: 75.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 10/18/2016] [Indexed: 12/03/2022] Open
Abstract
Background Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. Results We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper. Conclusion ClassyFire, in combination with ChemOnt (ClassyFire’s comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million “ClassyFire” classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0174-y) contains supplementary material, which is available to authorized users.
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Dejong CA, Chen GM, Li H, Johnston CW, Edwards MR, Rees PN, Skinnider MA, Webster ALH, Magarvey NA. Polyketide and nonribosomal peptide retro-biosynthesis and global gene cluster matching. Nat Chem Biol 2016; 12:1007-1014. [PMID: 27694801 DOI: 10.1038/nchembio.2188] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 07/20/2016] [Indexed: 11/09/2022]
Abstract
Polyketides (PKs) and nonribosomal peptides (NRPs) are profoundly important natural products, forming the foundations of many therapeutic regimes. Decades of research have revealed over 11,000 PK and NRP structures, and genome sequencing is uncovering new PK and NRP gene clusters at an unprecedented rate. However, only ∼10% of PK and NRPs are currently associated with gene clusters, and it is unclear how many of these orphan gene clusters encode previously isolated molecules. Therefore, to efficiently guide the discovery of new molecules, we must first systematically de-orphan emergent gene clusters from genomes. Here we provide to our knowledge the first comprehensive retro-biosynthetic program, generalized retro-biosynthetic assembly prediction engine (GRAPE), for PK and NRP families and introduce a computational pipeline, global alignment for natural products cheminformatics (GARLIC), to uncover how observed biosynthetic gene clusters relate to known molecules, leading to the identification of gene clusters that encode new molecules.
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Affiliation(s)
- Chris A Dejong
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Gregory M Chen
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Haoxin Li
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Chad W Johnston
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Mclean R Edwards
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Philip N Rees
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Michael A Skinnider
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Andrew L H Webster
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Nathan A Magarvey
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,Department of Chemistry and Chemical Biology, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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27
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Weskamp N. Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog. Mol Inform 2016; 35:286-92. [PMID: 27492243 DOI: 10.1002/minf.201600063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 06/09/2016] [Indexed: 11/10/2022]
Abstract
Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations.
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Affiliation(s)
- Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Discovery Research, Lead Identification and Optimization Support, Computational Chemistry, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany.
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28
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Berger B, Daniels NM, Yu YW. Computational Biology in the 21st Century: Scaling with Compressive Algorithms. COMMUNICATIONS OF THE ACM 2016; 59:72-80. [PMID: 28966343 PMCID: PMC5615407 DOI: 10.1145/2957324] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Algorithmic advances take advantage of the structure of massive biological data landscape.
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Affiliation(s)
- Bonnie Berger
- Department of Mathematics and EECS at Massachusetts Institute of Technology, Cambridge, MA
| | - Noah M Daniels
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA
| | - Y William Yu
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA
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29
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Abstract
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.
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30
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Mascotti ML, Juri Ayub M, Furnham N, Thornton JM, Laskowski RA. Chopping and Changing: the Evolution of the Flavin-dependent Monooxygenases. J Mol Biol 2016; 428:3131-46. [PMID: 27423402 PMCID: PMC4981433 DOI: 10.1016/j.jmb.2016.07.003] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 06/24/2016] [Accepted: 07/05/2016] [Indexed: 12/21/2022]
Abstract
Flavin-dependent monooxygenases play a variety of key physiological roles and are also very powerful biotechnological tools. These enzymes have been classified into eight different classes (A–H) based on their sequences and biochemical features. By combining structural and sequence analysis, and phylogenetic inference, we have explored the evolutionary history of classes A, B, E, F, and G and demonstrate that their multidomain architectures reflect their phylogenetic relationships, suggesting that the main evolutionary steps in their divergence are likely to have arisen from the recruitment of different domains. Additionally, the functional divergence within in each class appears to have been the result of other mechanisms such as a complex set of single-point mutations. Our results reinforce the idea that a main constraint on the evolution of cofactor-dependent enzymes is the functional binding of the cofactor. Additionally, a remarkable feature of this family is that the sequence of the key flavin adenine dinucleotide-binding domain is split into at least two parts in all classes studied here. We propose a complex set of evolutionary events that gave rise to the origin of the different classes within this family. Changes in domain architectures reflect the phylogeny of flavin monooxygenases. Recruitment of different domains has been the main force driving its evolution. A notable feature of flavin monooxygenases is that the flavin adenine dinucleotide-binding domain is split. Classes of monooxygenases emerged from an ancestral domain by structural changes.
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Affiliation(s)
- Maria Laura Mascotti
- IMIBIO-SL CONICET, Facultad de Química Bioquímica y Farmacia, Universidad Nacional de San Luis, Ejército de los Andes 950, San Luis D5700HHW, Argentina.
| | - Maximiliano Juri Ayub
- IMIBIO-SL CONICET, Facultad de Química Bioquímica y Farmacia, Universidad Nacional de San Luis, Ejército de los Andes 950, San Luis D5700HHW, Argentina
| | - Nicholas Furnham
- Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Janet M Thornton
- EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Roman A Laskowski
- EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
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31
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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32
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Son B, Jun SY, Seo H, Youn H, Yang HJ, Kim W, Kim HK, Kang C, Youn B. Inhibitory effect of traditional oriental medicine-derived monoamine oxidase B inhibitor on radioresistance of non-small cell lung cancer. Sci Rep 2016; 6:21986. [PMID: 26906215 PMCID: PMC4764943 DOI: 10.1038/srep21986] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 02/03/2016] [Indexed: 02/07/2023] Open
Abstract
Increased survival of cancer cells mediated by high levels of ionizing radiation (IR) reduces the effectiveness of radiation therapy for non-small cell lung cancer (NSCLC). In the present study, danshensu which is a selected component of traditional oriental medicine (TOM) compound was found to reduce the radioresistance of NSCLC by inhibiting the nuclear factor-κB (NF-κB) pathway. Of the various TOM compounds reported to inhibit the IR activation of NF-κB, danshensu was chosen as a final candidate based on the results of structural comparisons with human metabolites and monoamine oxidase B (MAOB) was identified as the putative target enzyme. Danshensu decreased the activation of NF-κB by inhibiting MAOB activity in A549 and NCI-H1299 NSCLC cells. Moreover, it suppressed IR-induced epithelial-to-mesenchymal transition, expressions of NF-κB-regulated prosurvival and proinflammatory genes, and in vivo radioresistance of mouse xenograft models. Taken together, this study shows that danshensu significantly reduces MAOB activity and attenuates NF-κB signaling to elicit the radiosensitization of NSCLC.
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Affiliation(s)
- Beomseok Son
- Department of Integrated Biological Science, Pusan National University, Busan, Republic of Korea
| | - Se Young Jun
- Department of Chemistry, Washington State University, Pullman, Washington, USA
| | - HyunJeong Seo
- Department of Integrated Biological Science, Pusan National University, Busan, Republic of Korea
| | - HyeSook Youn
- Nuclear Science Research Institute, Pusan National University, Busan, Republic of Korea
| | - Hee Jung Yang
- Department of Biological Sciences, Pusan National University, Busan, Republic of Korea
| | - Wanyeon Kim
- Nuclear Science Research Institute, Pusan National University, Busan, Republic of Korea.,Department of Biological Sciences, Pusan National University, Busan, Republic of Korea
| | - Hyung Kook Kim
- Department of Nanomaterial Engineering and Nanoconvergence Technology, Pusan National University, Miryang, Republic of Korea
| | - ChulHee Kang
- Department of Chemistry, Washington State University, Pullman, Washington, USA
| | - BuHyun Youn
- Department of Integrated Biological Science, Pusan National University, Busan, Republic of Korea.,Department of Chemistry, Washington State University, Pullman, Washington, USA.,Nuclear Science Research Institute, Pusan National University, Busan, Republic of Korea.,Department of Biological Sciences, Pusan National University, Busan, Republic of Korea
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33
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Rahman SA, Torrance G, Baldacci L, Martínez Cuesta S, Fenninger F, Gopal N, Choudhary S, May JW, Holliday GL, Steinbeck C, Thornton JM. Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics 2016; 32:2065-6. [PMID: 27153692 PMCID: PMC4920114 DOI: 10.1093/bioinformatics/btw096] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 02/12/2016] [Indexed: 01/22/2023] Open
Abstract
Summary: Extracting chemical features like Atom–Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. Availability and implementation: This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder Contact: asad@ebi.ac.uk or s9asad@gmail.com
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Affiliation(s)
- Syed Asad Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Congenica Ltd, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Gilliean Torrance
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Lorenzo Baldacci
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK DISI, University of Bologna, V.Le Risorgimento 2, Bologna, Italy
| | - Sergio Martínez Cuesta
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Franz Fenninger
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Michael Smith Laboratories, the University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Nimish Gopal
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Saket Choudhary
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - John W May
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Innovation Centre (Unit 23), Cambridge Science Park, NextMove Software Ltd, Cambridge CB4 0EY, UK
| | - Gemma L Holliday
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Bioengineering, UCSF School of Pharmacy, San Francisco, CA 94158, USA
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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34
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Phillips MB, Leonard JA, Grulke CM, Chang DT, Edwards SW, Brooks R, Goldsmith MR, El-Masri H, Tan YM. A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:53-60. [PMID: 25978103 PMCID: PMC4710605 DOI: 10.1289/ehp.1409450] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 05/13/2015] [Indexed: 05/28/2023]
Abstract
BACKGROUND Adverse outcome pathways (AOPs) link adverse effects in individuals or populations to a molecular initiating event (MIE) that can be quantified using in vitro methods. Practical application of AOPs in chemical-specific risk assessment requires incorporation of knowledge on exposure, along with absorption, distribution, metabolism, and excretion (ADME) properties of chemicals. OBJECTIVES We developed a conceptual workflow to examine exposure and ADME properties in relation to an MIE. The utility of this workflow was evaluated using a previously established AOP, acetylcholinesterase (AChE) inhibition. METHODS Thirty chemicals found to inhibit human AChE in the ToxCast™ assay were examined with respect to their exposure, absorption potential, and ability to cross the blood-brain barrier (BBB). Structures of active chemicals were compared against structures of 1,029 inactive chemicals to detect possible parent compounds that might have active metabolites. RESULTS Application of the workflow screened 10 "low-priority" chemicals of 30 active chemicals. Fifty-two of the 1,029 inactive chemicals exhibited a similarity threshold of ≥ 75% with their nearest active neighbors. Of these 52 compounds, 30 were excluded due to poor absorption or distribution. The remaining 22 compounds may inhibit AChE in vivo either directly or as a result of metabolic activation. CONCLUSIONS The incorporation of exposure and ADME properties into the conceptual workflow eliminated 10 "low-priority" chemicals that may otherwise have undergone additional, resource-consuming analyses. Our workflow also increased confidence in interpretation of in vitro results by identifying possible "false negatives." CITATION Phillips MB, Leonard JA, Grulke CM, Chang DT, Edwards SW, Brooks R, Goldsmith MR, El-Masri H, Tan YM. 2016. A workflow to investigate exposure and pharmacokinetic influences on high-throughput in vitro chemical screening based on adverse outcome pathways. Environ Health Perspect 124:53-60; http://dx.doi.org/10.1289/ehp.1409450.
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Affiliation(s)
- Martin B. Phillips
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Jeremy A. Leonard
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | | | | | - Stephen W. Edwards
- National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Raina Brooks
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Hisham El-Masri
- National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Yu-Mei Tan
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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35
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Dufresne Y, Noé L, Leclère V, Pupin M. Smiles2Monomers: a link between chemical and biological structures for polymers. J Cheminform 2015; 7:62. [PMID: 26715946 PMCID: PMC4693424 DOI: 10.1186/s13321-015-0111-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 12/06/2015] [Indexed: 12/17/2022] Open
Abstract
Background The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure. Results Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s. Conclusion s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0111-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yoann Dufresne
- Univ. Lille, CNRS, Centrale Lille, UMR 9189-CRIStAL-Centre de Recherche en Informatique Signal et Automatique de Lille, 59000 Lille, France ; Inria Lille Nord Europe, Bonsai team, Parc scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d'Ascq, France
| | - Laurent Noé
- Univ. Lille, CNRS, Centrale Lille, UMR 9189-CRIStAL-Centre de Recherche en Informatique Signal et Automatique de Lille, 59000 Lille, France ; Inria Lille Nord Europe, Bonsai team, Parc scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d'Ascq, France
| | - Valérie Leclère
- Univ. Lille, CNRS, Centrale Lille, UMR 9189-CRIStAL-Centre de Recherche en Informatique Signal et Automatique de Lille, 59000 Lille, France ; Inria Lille Nord Europe, Bonsai team, Parc scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d'Ascq, France ; Univ. Lille, INRA, ISA, Univ. Artois, Univ. Littoral Côte d'Opale, EA 7394 - ICV - Institut Charles Viollette, 59000 Lille, France
| | - Maude Pupin
- Univ. Lille, CNRS, Centrale Lille, UMR 9189-CRIStAL-Centre de Recherche en Informatique Signal et Automatique de Lille, 59000 Lille, France ; Inria Lille Nord Europe, Bonsai team, Parc scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d'Ascq, France
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36
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Large-Scale Analysis Exploring Evolution of Catalytic Machineries and Mechanisms in Enzyme Superfamilies. J Mol Biol 2015; 428:253-267. [PMID: 26585402 PMCID: PMC4751976 DOI: 10.1016/j.jmb.2015.11.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 10/05/2015] [Accepted: 11/10/2015] [Indexed: 01/28/2023]
Abstract
Enzymes, as biological catalysts, form the basis of all forms of life. How these proteins have evolved their functions remains a fundamental question in biology. Over 100 years of detailed biochemistry studies, combined with the large volumes of sequence and protein structural data now available, means that we are able to perform large-scale analyses to address this question. Using a range of computational tools and resources, we have compiled information on all experimentally annotated changes in enzyme function within 379 structurally defined protein domain superfamilies, linking the changes observed in functions during evolution to changes in reaction chemistry. Many superfamilies show changes in function at some level, although one function often dominates one superfamily. We use quantitative measures of changes in reaction chemistry to reveal the various types of chemical changes occurring during evolution and to exemplify these by detailed examples. Additionally, we use structural information of the enzymes active site to examine how different superfamilies have changed their catalytic machinery during evolution. Some superfamilies have changed the reactions they perform without changing catalytic machinery. In others, large changes of enzyme function, in terms of both overall chemistry and substrate specificity, have been brought about by significant changes in catalytic machinery. Interestingly, in some superfamilies, relatives perform similar functions but with different catalytic machineries. This analysis highlights characteristics of functional evolution across a wide range of superfamilies, providing insights that will be useful in predicting the function of uncharacterised sequences and the design of new synthetic enzymes. Examining how enzyme function evolves using sequence, structure, and reaction mechanism data. Quantifying changes in reaction mechanisms reveals how function has diverged in many superfamilies. Homologous domains frequently use different catalytic residues, which sometimes perform the same enzyme chemistry. This large-scale analysis has significance in protein function prediction and enzyme design.
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37
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Ibrahim TM, Bauer MR, Dörr A, Veyisoglu E, Boeckler FM. pROC-Chemotype Plots Enhance the Interpretability of Benchmarking Results in Structure-Based Virtual Screening. J Chem Inf Model 2015; 55:2297-307. [DOI: 10.1021/acs.jcim.5b00475] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tamer M. Ibrahim
- Laboratory
for Molecular Design and Pharmaceutical Biophysics, Department of
Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical
Sciences, Eberhard Karls Universität Tübingen, Auf
der Morgenstelle 8, 72076 Tübingen, Germany
| | - Matthias R. Bauer
- Laboratory
for Molecular Design and Pharmaceutical Biophysics, Department of
Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical
Sciences, Eberhard Karls Universität Tübingen, Auf
der Morgenstelle 8, 72076 Tübingen, Germany
| | - Alexander Dörr
- Center
for Bioinformatics Tübingen (ZBIT), Eberhard Karls University Tübingen, Sand 1, 72076 Tübingen, Germany
| | - Erdem Veyisoglu
- Laboratory
for Molecular Design and Pharmaceutical Biophysics, Department of
Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical
Sciences, Eberhard Karls Universität Tübingen, Auf
der Morgenstelle 8, 72076 Tübingen, Germany
| | - Frank M. Boeckler
- Laboratory
for Molecular Design and Pharmaceutical Biophysics, Department of
Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical
Sciences, Eberhard Karls Universität Tübingen, Auf
der Morgenstelle 8, 72076 Tübingen, Germany
- Center
for Bioinformatics Tübingen (ZBIT), Eberhard Karls University Tübingen, Sand 1, 72076 Tübingen, Germany
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38
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Abstract
Many data sets exhibit well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here we introduce a framework for similarity search based on characterizing a data set's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the data set is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains-high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND (3700x BLASTX)), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve 'compressive omics,' and the general theory can be readily applied to data science problems outside of biology. Source code: http://gems.csail.mit.edu.
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Affiliation(s)
- Y William Yu
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 ; Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Noah M Daniels
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 ; Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - David Christian Danko
- Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Bonnie Berger
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 ; Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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Amani P, Sneyd T, Preston S, Young ND, Mason L, Bailey UM, Baell J, Camp D, Gasser RB, Gorse AD, Taylor P, Hofmann A. A practical Java tool for small-molecule compound appraisal. J Cheminform 2015; 7:28. [PMID: 26082805 PMCID: PMC4469258 DOI: 10.1186/s13321-015-0079-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 05/27/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The increased use of small-molecule compound screening by new users from a variety of different academic backgrounds calls for adequate software to administer, appraise, analyse and exchange information obtained from screening experiments. While software and spreadsheet solutions exist, there is a need for software that can be easily deployed and is convenient to use. RESULTS The Java application cApp addresses this need and aids in the handling and storage of information on small-molecule compounds. The software is intended for the appraisal of compounds with respect to their physico-chemical properties, analysis in relation to adherence to likeness rules as well as recognition of pan-assay interference components and cross-linking with identical entries in the PubChem Compound Database. Results are displayed in a tabular form in a graphical interface, but can also be written in an HTML or PDF format. The output of data in ASCII format allows for further processing of data using other suitable programs. Other features include similarity searches against user-provided compound libraries and the PubChem Compound Database, as well as compound clustering based on a MaxMin algorithm. CONCLUSIONS cApp is a personal database solution for small-molecule compounds which can handle all major chemical formats. Being a standalone software, it has no other dependency than the Java virtual machine and is thus conveniently deployed. It streamlines the analysis of molecules with respect to physico-chemical properties and drug discovery criteria; cApp is distributed under the GNU Affero General Public License version 3 and available from http://www.structuralchemistry.org/pcsb/. To download cApp, users will be asked for their name, institution and email address. A detailed manual can also be downloaded from this site, and online tutorials are available at http://www.structuralchemistry.org/pcsb/capp.php.
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Affiliation(s)
- Parisa Amani
- />Structural Chemistry Program, Eskitis Institute, Griffith University, Nathan, QLD Australia
| | - Todd Sneyd
- />Structural Chemistry Program, Eskitis Institute, Griffith University, Nathan, QLD Australia
| | - Sarah Preston
- />Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC Australia
| | - Neil D Young
- />Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC Australia
| | - Lyndel Mason
- />Structural Chemistry Program, Eskitis Institute, Griffith University, Nathan, QLD Australia
| | - Ulla-Maja Bailey
- />Structural Chemistry Program, Eskitis Institute, Griffith University, Nathan, QLD Australia
| | - Jonathan Baell
- />Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences (MIPS), Monash University, Parkville, VIC Australia
| | - David Camp
- />Griffith School of Environment, Griffith University, Nathan, QLD Australia
| | - Robin B Gasser
- />Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC Australia
| | - Alain-Dominique Gorse
- />Queensland Facility for Advanced Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD Australia
| | - Paul Taylor
- />School of Biological Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Andreas Hofmann
- />Structural Chemistry Program, Eskitis Institute, Griffith University, Nathan, QLD Australia
- />Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC Australia
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Abstract
Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.
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Affiliation(s)
- Péter Englert
- †Department of Algorithms and Applications, Eötvös Loránd University, Budapest 1117, Hungary
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Gfeller D, Zoete V. Protein homology reveals new targets for bioactive small molecules. Bioinformatics 2015; 31:2721-7. [PMID: 25900917 DOI: 10.1093/bioinformatics/btv214] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 04/14/2015] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. RESULTS Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. AVAILABILITY AND IMPLEMENTATION Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. CONTACT david.gfeller@unil.ch or vincent.zoete@isb-sib.ch. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Gfeller
- Department of Fundamental Oncology, Ludwig Center for Cancer Research, University of Lausanne, 1066 Epalinges, Switzerland and Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
| | - Vincent Zoete
- Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
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Hamdalla MA, Ammar RA, Rajasekaran S. A molecular structure matching approach to efficient identification of endogenous mammalian biochemical structures. BMC Bioinformatics 2015; 16 Suppl 5:S11. [PMID: 25859612 PMCID: PMC4402589 DOI: 10.1186/1471-2105-16-s5-s11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Metabolomics is the study of small molecules, called metabolites, of a cell, tissue or organism. It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression. A major challenge in metabolomics research is the structural identification of unknown biochemical compounds in complex biofluids. In this paper we present an efficient cheminformatics tool, BioSMXpress that uses known endogenous mammalian biochemicals 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 BioSMXpress identifies endogenous mammalian biochemical structures with high accuracy. BioSMXpress is 8 times faster than our previous work BioSM without compromising the accuracy of the predictions made. BioSMXpress is freely available at http://engr.uconn.edu/~rajasek/BioSMXpress.zip
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44
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Hamdalla MA, Rajasekaran S, Grant DF, Măndoiu II. Metabolic pathway predictions for metabolomics: a molecular structure matching approach. J Chem Inf Model 2015; 55:709-18. [PMID: 25668446 DOI: 10.1021/ci500517v] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html .
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Affiliation(s)
- Mai A Hamdalla
- ‡Computer Science Department, Helwan University, Cairo, Egypt
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45
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Weber J, Achenbach J, Moser D, Proschak E. VAMMPIRE-LORD: A Web Server for Straightforward Lead Optimization Using Matched Molecular Pairs. J Chem Inf Model 2015; 55:207-13. [DOI: 10.1021/ci5005256] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Julia Weber
- Institute of Pharmaceutical
Chemistry, Goethe University, Frankfurt 60438, Germany
| | - Janosch Achenbach
- Institute of Pharmaceutical
Chemistry, Goethe University, Frankfurt 60438, Germany
| | - Daniel Moser
- Institute of Pharmaceutical
Chemistry, Goethe University, Frankfurt 60438, Germany
| | - Ewgenij Proschak
- Institute of Pharmaceutical
Chemistry, Goethe University, Frankfurt 60438, Germany
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Kumar A, Maranas CD. CLCA: Maximum Common Molecular Substructure Queries within the MetRxn Database. J Chem Inf Model 2014; 54:3417-38. [DOI: 10.1021/ci5003922] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Akhil Kumar
- The
Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Costas D. Maranas
- Department
of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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Brylinski M. eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol 2014; 10:e1003829. [PMID: 25232727 PMCID: PMC4168975 DOI: 10.1371/journal.pcbi.1003829] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 07/24/2014] [Indexed: 01/26/2023] Open
Abstract
Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
- * E-mail:
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Tang GW, Altman RB. Knowledge-based fragment binding prediction. PLoS Comput Biol 2014; 10:e1003589. [PMID: 24762971 PMCID: PMC3998881 DOI: 10.1371/journal.pcbi.1003589] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 03/11/2014] [Indexed: 11/18/2022] Open
Abstract
Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.
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Affiliation(s)
- Grace W. Tang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Genetics, Stanford University, Stanford, California, United States of America
- * E-mail:
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Alderson RG, De Ferrari L, Mavridis L, McDonagh JL, Mitchell JBO, Nath N. Enzyme informatics. Curr Top Med Chem 2014; 12:1911-23. [PMID: 23116471 DOI: 10.2174/156802612804547353] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2012] [Revised: 09/12/2012] [Accepted: 09/15/2012] [Indexed: 12/18/2022]
Abstract
Over the last 50 years, sequencing, structural biology and bioinformatics have completely revolutionised biomolecular science, with millions of sequences and tens of thousands of three dimensional structures becoming available. The bioinformatics of enzymes is well served by, mostly free, online databases. BRENDA describes the chemistry, substrate specificity, kinetics, preparation and biological sources of enzymes, while KEGG is valuable for understanding enzymes and metabolic pathways. EzCatDB, SFLD and MACiE are key repositories for data on the chemical mechanisms by which enzymes operate. At the current rate of genome sequencing and manual annotation, human curation will never finish the functional annotation of the ever-expanding list of known enzymes. Hence there is an increasing need for automated annotation, though it is not yet widespread for enzyme data. In contrast, functional ontologies such as the Gene Ontology already profit from automation. Despite our growing understanding of enzyme structure and dynamics, we are only beginning to be able to design novel enzymes. One can now begin to trace the functional evolution of enzymes using phylogenetics. The ability of enzymes to perform secondary functions, albeit relatively inefficiently, gives clues as to how enzyme function evolves. Substrate promiscuity in enzymes is one example of imperfect specificity in protein-ligand interactions. Similarly, most drugs bind to more than one protein target. This may sometimes result in helpful polypharmacology as a drug modulates plural targets, but also often leads to adverse side-effects. Many chemoinformatics approaches can be used to model the interactions between druglike molecules and proteins in silico. We can even use quantum chemical techniques like DFT and QM/MM to compute the structural and energetic course of enzyme catalysed chemical reaction mechanisms, including a full description of bond making and breaking.
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Affiliation(s)
- Rosanna G Alderson
- Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, Scotland, UK
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50
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Kurbatova N, Chartier M, Zylber MI, Najmanovich R. IsoCleft Finder - a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities. F1000Res 2014; 2:117. [PMID: 24555058 DOI: 10.12688/f1000research.2-117.v1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/30/2013] [Indexed: 11/20/2022] Open
Abstract
IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder.
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Affiliation(s)
- Natalja Kurbatova
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
| | - Matthieu Chartier
- Department of Biochemistry, Université de Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - María Inés Zylber
- Department of Biochemistry, Université de Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - Rafael Najmanovich
- Department of Biochemistry, Université de Sherbrooke, Sherbrooke, J1H 5N4, Canada
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