Antileishmanial activity of polycyclic derivatives

Sarciron, Marie‐Elisabeth; Terreux, Raphaël; Prieto, Yolanda; Cortés, Manuel; Cuéllar, Mauricio; Tapia, Ricardo A.; Domard, M.; Walchshofer, Nadia; Pétavy, A. F.

doi:10.1051/parasite/2005123251

Cited by 4 publications

(1 citation statement)

References 37 publications

(31 reference statements)

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“…The study of peptides found on the PMFs of new proteins may become an interesting source to discover new peptides with potential use as drug, in vaccine design, or as disease biomarkers. In particular, toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new drugs or drug molecular targets in Leishmania species including L. infantum and L. major, both important pathogens (Chenik et al, 2006;Dea-Ayuela et al, 2008;Roldos et al, 2008;Sarciron et al, 2005). In the two previous examples, we used lattices to study the sequences and MD results of peptides found in a dynein of L. major.…”

Section: Lattices For Ms Of Peptide Mass Fingerprintsmentioning

confidence: 99%

Generalized lattice graphs for 2D-visualization of biological information

González-Dı́az

Pérez-Montoto

Duardo-Sánchez

et al. 2009

Journal of Theoretical Biology

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A c c e p t e d m a n u s c r i p t2 Abstract Several graph representations have been introduced for different data in theoretical biology. For instance, Complex Networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new Lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: Protein sequence; Mass Spectra (MS) of protein Peptide Mass Fingerprints (PMF); Molecular Dynamic Trajectory (MDTs) from structural studies; mRNA Microarray data; Single Nucleotide Polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein Polymorphisms and Protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of graph theory in theoretical biology and other areas of biomedical sciences.Keywords: Graph theory; Complex Networks; Proteomics; Mass Spectrometry; Leishmaniosis; 2D Electrophoresis; Parasite population Polymorphism; Single Nucleotide Polymorphism; Schizophrenia; Microarray; Cancer; Patents & Copyright studies. A c c e p t e d m a n u s c r i p t 3 IntroductionSeveral graph representations have been introduced for different data in theoretical biology. For instance, Complex Networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. Complex networks are made up of nodes and edges/arcs (node-node connections or links). Drugs, genes, RNAs, proteins, organisms, brain cortex regions, diseases, patients or environmental systems may play the role of nodes. In general, the edges represent similarity/dissimilarity relationships between the nodes. In Complex Networks, both nodes and edges are placed generally in space without any geometrical constrains; nodes do not need spatial coordinates and edges have not a specific length or shape (Barabasi and Oltvai, 2004;Boccaletti et al., 2006;Estrada, 2006). In addition, Randic, Nandy, Basak, Liao, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and sometimes adopts final geometrical shapes that resemble lattice...

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Section: Lattices For Ms Of Peptide Mass Fingerprintsmentioning

confidence: 99%

Generalized lattice graphs for 2D-visualization of biological information

González-Dı́az

Pérez-Montoto

Duardo-Sánchez

et al. 2009

Journal of Theoretical Biology

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2-Phenylaminonaphthoquinones and related compounds: Synthesis, trypanocidal and cytotoxic activities

Sieveking

Thomas

Estévez

et al. 2014

Bioorganic & Medicinal Chemistry

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Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Pérez-Montoto

Dea-Ayuela

Bolás‐Fernández

et al. 2009

Polymer

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a b s t r a c tSince the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values x k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The x k (L) values are invariants of a Markov matrix ( 1 P), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective x k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy ¼ 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

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Antileishmanial activity of polycyclic derivatives

Cited by 4 publications

References 37 publications

Generalized lattice graphs for 2D-visualization of biological information

Generalized lattice graphs for 2D-visualization of biological information

2-Phenylaminonaphthoquinones and related compounds: Synthesis, trypanocidal and cytotoxic activities

Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

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